The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644
# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR) # see a note above
library(orchaRd) # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans) # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices) # reqired for using base and ggplots together
# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e) {
lnRR <- log(Me/Mc) + 0.5 * ((aCV2e/Ne) - (aCV2c/Nc))
lnRR
}
# calculating lnRR's sampling variance from independent designs
var_lnRR_ind <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e) {
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne)
var_lnRR
}
# calculating lnRR's sampling variance from dependent designs
var_lnRR_dep <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5) {
var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 2 * rho * ((aCV2c * aCV2e)/sqrt(Nc * Ne))
var_lnRR
}
# Mc: Concentration of PFAS of the raw (control) sample Nc: Sample size of the
# raw (control) sample Me: Concentration of PFAS of the cooked (experimental)
# sample Ne: Sample size of the cooked (experimental) sample aCV2c: Mean
# coefficient of variation of the raw (control) samples aCV2e: Mean coefficient
# of variation of the cooked (experimental) samplesprocessed_data <- read.csv(here("data", "Rawdata_updated_2.csv"))
dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples
dat <- dat %>% filter(Species_Scientific != "?") # Remove species without species names
dat <- dat %>% filter(PFAS_type != "PFOS_Total") # Remove species without species names
#### Ratio_liquid_fish with "0" for the dry cooking category
dat<-dat %>% mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category =="No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is "No liquid", otherwise keep the same value of Ratio_liquid_fish
# taking out data without species information
# arrange(select(dat, Cooking_Category, Ratio_liquid_fish, Ratio_liquid_fish_0), Cooking_Category) # Checking everything is fine
dat$Temperature_in_Celsius[dat$Temperature_in_Celsius=="?"] <- NA # Replace "?" by missing values
dat$Temperature_in_Celsius <- as.numeric(dat$Temperature_in_Celsius) # Convert integer to numeric
dat$Study_ID[dat$Effect_ID=="E557"] # Two effect sizes have the same unique identifier## [1] "F013"
dat$Effect_ID[dat$Effect_ID=="E557"&dat$Study_ID=="F013"]<- "E613"
kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Cohort_comment_2 | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F001 | Alves_2017 | 2017 | Portugal | E001 | Flounder | Platichthys flesus | vertebrate | marine fish | 7.43 | PFOS | 8 | linear | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C001 | Shared control | NA | 25.000000 | 1 | ng/g | 24.0000000 | NA | 1.5280000 | sd | technical | 25.000000 | 1 | 22.0000000 | NA | 1.53 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F001 | Alves_2017 | 2017 | Portugal | E002 | Mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Steaming | water-based | NA | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C002 | Shared control | NA | 25.000000 | 1 | ng/g | 3.1000000 | NA | 0.2120000 | sd | technical | 25.000000 | 1 | 2.9000000 | NA | 0.141 | technical | 2 | ng/g | <0.1 | <0.2 | Dependent | Table 3 | No | Authors replied | ML - ok | NA | NA | NA |
| F002 | Barbosa_2018 | 2018 | Portugal | E003 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFUnDA | 11 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 13.3018868 | NA | 0.0471698 | sd | technical | 25.000000 | 1 | 4.1510000 | NA | 0.094 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E004 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFDoDA | 12 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 3.5731707 | NA | 0.0243902 | sd | technical | 25.000000 | 1 | 3.2070000 | NA | 0.024 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E005 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFTrA | 13 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 6.5283019 | NA | 0.0754717 | sd | technical | 25.000000 | 1 | 10.0380000 | NA | 0.075 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E006 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFTA | 14 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 1.3736842 | NA | 0.0157895 | sd | technical | 25.000000 | 1 | 1.3320000 | NA | 0.021 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E007 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 0.6467391 | NA | 0.0054348 | sd | technical | 25.000000 | 1 | 0.3020000 | NA | 0.008 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E008 | Skipjack tuna | Katsuwonus pelamis | vertebrate | marine fish | 16.86 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C003 | Shared control | NA | 25.000000 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 25.000000 | 1 | 0.0870000 | NA | 0.013 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E009 | European plaice | Pleuronectes platessa | vertebrate | marine fish | 8.70 | PFOS | 8 | total | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C004 | Shared control | NA | 25.000000 | 1 | ng/g | 0.2472826 | NA | 0.0081522 | sd | technical | 25.000000 | 1 | 0.2530000 | NA | 0.005 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E010 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.77 | PFBA | 3 | NA | No | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | Shared control | NA | 50.000000 | 1 | ng/g | 0.0250000 | <LOQ | NA | sd | technical | 50.000000 | 1 | 0.2080000 | NA | 0.009 | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied. We assume that the mussels were cut in half. The paper does not state this clearly but it was our assumptions that they did not use the whole mussle for each treatment. | NA | NA | NA | |
| F002 | Barbosa_2018 | 2018 | Portugal | E011 | blue mussel | Mytilus edulis | invertebrate | mollusca | 6.77 | PFDA | 10 | NA | Yes | Steaming | water-based | wrapped up in aluminum foil | 105 | 900 | Yes | No | NA | NA | NA | NA | NA | C005 | Shared control | NA | 50.000000 | 1 | ng/g | 0.0241860 | NA | 0.0074419 | sd | technical | 50.000000 | 1 | 0.0250000 | <LOQ | NA | technical | 2 | ng/g | <0.01 | <0.04 | Dependent | Table 2 | Yes | Authors replied. We assume that the mussels were cut in half. The paper does not state this clearly but it was our assumptions that they did not use the whole mussle for each treatment. | NA | NA | NA | |
| F003 | Bhavsar_2014 | 2014 | Canada | E012 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0860000 | NA | 0.135 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | ML | 0.0950000 | 0.135 | 0.1042160 | |
| F003 | Bhavsar_2014 | 2014 | Canada | E013 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1920000 | NA | 0.266 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.266 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E014 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2340000 | NA | 0.291 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.291 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E015 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.1010000 | NA | 0.095 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.095 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E016 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2590000 | NA | 0.241 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.241 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E017 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.073 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.073 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E019 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.5600000 | NA | 18 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 18 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E020 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3970000 | NA | 0.433 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.433 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E021 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.002 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.002 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E022 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 14.40 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1042160 | 105.5500 | C006 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0100000 | NA | 0.016 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.016 | 0.1042160 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E023 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.118 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.118 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E024 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1900000 | NA | 0.232 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.232 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E025 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2560000 | NA | 0.31 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.31 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E026 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.1000000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.08 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E027 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2850000 | NA | 0.234 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.234 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E028 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.071 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.071 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E030 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.4500000 | NA | 15.63 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 15.63 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E031 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3920000 | NA | 0.359 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.359 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E032 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.003 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.003 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E033 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 19.68 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0033813 | 100.8500 | C007 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0140000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.022 | 0.0033813 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E034 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0950000 | sd | biological | 5.000000 | 5 | 0.0780000 | NA | 0.114 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.114 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E035 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1560000 | NA | 0.1970000 | sd | biological | 5.000000 | 5 | 0.1820000 | NA | 0.222 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1970000 | 0.222 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E036 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1860000 | NA | 0.2250000 | sd | biological | 5.000000 | 5 | 0.2270000 | NA | 0.255 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2250000 | 0.255 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E037 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0730000 | sd | biological | 5.000000 | 5 | 0.0960000 | NA | 0.081 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0730000 | 0.081 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E038 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2150000 | NA | 0.1830000 | sd | biological | 5.000000 | 5 | 0.2750000 | NA | 0.216 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1830000 | 0.216 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E039 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0760000 | NA | 0.0550000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.067 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.067 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E041 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 12.7000000 | NA | 12.6100000 | sd | biological | 5.000000 | 5 | 16.0300000 | NA | 15.19 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 12.6100000 | 15.19 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E042 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.3030000 | NA | 0.2840000 | sd | biological | 5.000000 | 5 | 0.3930000 | NA | 0.369 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2840000 | 0.369 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E043 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0030000 | NA | 0.0030000 | sd | biological | 5.000000 | 5 | 0.0020000 | NA | 0.003 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0030000 | 0.003 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E044 | Chinook salmon | Oncorhynchus tshawytscha | vertebrate | marine fish | 18.68 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1000364 | 109.9600 | C008 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0170000 | NA | 0.0230000 | sd | biological | 5.000000 | 5 | 0.0130000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0230000 | 0.022 | 0.1000364 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E045 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.0990000 | NA | 0.022 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.022 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E046 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5660000 | NA | 0.138 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.138 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E047 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8040000 | NA | 0.167 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.167 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E048 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.0960000 | NA | 0.396 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.396 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E049 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.7740000 | NA | 0.332 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.332 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E050 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.1400000 | NA | 0.874 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.874 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E051 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.3410000 | NA | 0.391 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.391 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E052 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 30.5200000 | NA | 9.254 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 9.254 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E053 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0840000 | NA | 0.571 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.571 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E054 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C009 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1050000 | NA | 0.06 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.06 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E055 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 15.47 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.1114940 | 98.6600 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1800000 | NA | 0.084 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.084 | 0.1114940 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E056 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.1050000 | NA | 0.037 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.037 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E057 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5480000 | NA | 0.121 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.121 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E058 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8480000 | NA | 0.155 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.155 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E059 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.1080000 | NA | 0.404 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.404 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E060 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.8280000 | NA | 0.418 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.418 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E061 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.1150000 | NA | 0.769 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.769 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E062 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.2910000 | NA | 0.346 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.346 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E063 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 28.3700000 | NA | 11.99 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 11.99 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E064 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0450000 | NA | 0.623 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.623 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E065 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1170000 | NA | 0.073 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.073 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E066 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 19.68 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.341 | 0.341 | 0.0034647 | 98.4200 | C010 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1900000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.08 | 0.0034647 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E067 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0920000 | NA | 0.0300000 | sd | biological | 5.000000 | 5 | 0.1010000 | NA | 0.035 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0300000 | 0.035 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E068 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.5180000 | NA | 0.1070000 | sd | biological | 5.000000 | 5 | 0.5690000 | NA | 0.108 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1070000 | 0.108 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E069 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7120000 | NA | 0.1580000 | sd | biological | 5.000000 | 5 | 0.8300000 | NA | 0.13 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1580000 | 0.13 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E070 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9890000 | NA | 0.3170000 | sd | biological | 5.000000 | 5 | 1.0440000 | NA | 0.356 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3170000 | 0.356 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E071 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.7790000 | NA | 0.4400000 | sd | biological | 5.000000 | 5 | 0.7460000 | NA | 0.283 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.4400000 | 0.283 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E072 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9510000 | NA | 0.6470000 | sd | biological | 5.000000 | 5 | 1.0670000 | NA | 0.754 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.6470000 | 0.754 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E073 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2920000 | NA | 0.3190000 | sd | biological | 5.000000 | 5 | 0.3590000 | NA | 0.428 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.3190000 | 0.428 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E074 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 27.1700000 | NA | 7.7680000 | sd | biological | 5.000000 | 5 | 28.1100000 | NA | 10.93 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 7.7680000 | 10.93 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E075 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.9110000 | NA | 0.5320000 | sd | biological | 5.000000 | 5 | 1.0900000 | NA | 0.618 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.5320000 | 0.618 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E076 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0980000 | NA | 0.0600000 | sd | biological | 5.000000 | 5 | 0.1060000 | NA | 0.065 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0600000 | 0.065 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E077 | Common carp | Cyprinus carpio | vertebrate | freshwater fish | 14.91 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.1099340 | 100.0600 | C011 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1670000 | NA | 0.0770000 | sd | biological | 5.000000 | 5 | 0.1880000 | NA | 0.075 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0770000 | 0.075 | 0.1099340 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E078 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3700000 | NA | 0.189 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.189 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E079 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.5100000 | NA | 0.232 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.232 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E080 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.6850000 | NA | 0.293 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.293 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E081 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2210000 | NA | 0.114 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.114 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E082 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.4840000 | NA | 0.264 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.264 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E083 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1370000 | NA | 0.051 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.051 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E084 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2480000 | NA | 0.061 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.061 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E085 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 20.5100000 | NA | 6.752 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 6.752 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E086 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.4740000 | NA | 0.196 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.196 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E087 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0020000 | NA | 0.002 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.002 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E088 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 10.13 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0517671 | 212.4900 | C012 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0180000 | NA | 0.009 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.009 | 0.0517671 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E089 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3580000 | NA | 0.17 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.17 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E090 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.5280000 | NA | 0.233 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.233 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E091 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.7250000 | NA | 0.345 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.345 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E092 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2370000 | NA | 0.111 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.111 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E093 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.5580000 | NA | 0.28 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.28 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E094 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1490000 | NA | 0.068 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.068 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E095 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2630000 | NA | 0.087 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.087 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E096 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 22.1100000 | NA | 7.897 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 7.897 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E097 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.5600000 | NA | 0.226 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.226 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E098 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0120000 | NA | 0.018 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.018 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E099 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 15.23 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.528 | 0.528 | 0.0026586 | 198.6000 | C013 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0160000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.006 | 0.0026586 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E100 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2980000 | NA | 0.1430000 | sd | biological | 4.000000 | 4 | 0.3740000 | NA | 0.181 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1430000 | 0.181 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E101 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4230000 | NA | 0.1860000 | sd | biological | 4.000000 | 4 | 0.4930000 | NA | 0.207 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1860000 | 0.207 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E102 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.5600000 | NA | 0.2510000 | sd | biological | 4.000000 | 4 | 0.6830000 | NA | 0.286 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2510000 | 0.286 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E103 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1980000 | NA | 0.0950000 | sd | biological | 4.000000 | 4 | 0.2320000 | NA | 0.103 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0950000 | 0.103 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E104 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4610000 | NA | 0.2170000 | sd | biological | 4.000000 | 4 | 0.5190000 | NA | 0.212 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.2170000 | 0.212 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E105 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.1280000 | NA | 0.0510000 | sd | biological | 4.000000 | 4 | 0.1290000 | NA | 0.045 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0510000 | 0.045 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E106 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.2580000 | NA | 0.0550000 | sd | biological | 4.000000 | 4 | 0.2450000 | NA | 0.077 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0550000 | 0.077 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E107 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 18.1800000 | NA | 6.6860000 | sd | biological | 4.000000 | 4 | 21.6700000 | NA | 8.008 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 6.6860000 | 8.008 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E108 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.4560000 | NA | 0.1770000 | sd | biological | 4.000000 | 4 | 0.5160000 | NA | 0.244 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.1770000 | 0.244 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E109 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0020000 | NA | 0.0010000 | sd | biological | 4.000000 | 4 | 0.0020000 | NA | 0.001 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0010000 | 0.001 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E110 | Lake trout | Salvelinus namaycush | vertebrate | freshwater fish | 11.53 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0511604 | 215.0100 | C014 | Shared control | NA | 4.000000 | 4 | ng/g | 0.0170000 | NA | 0.0090000 | sd | biological | 4.000000 | 4 | 0.0160000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.006 | 0.0511604 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E111 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFNA | 9 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0790000 | NA | 0.023 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.023 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E112 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDA | 10 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3490000 | NA | 0.094 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.094 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E113 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFUnDA | 11 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3330000 | NA | 0.091 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.091 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E114 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDoDA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1330000 | NA | 0.012 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.012 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E115 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFTrA | 13 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.1800000 | NA | 0.021 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.021 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E116 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFTA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0930000 | NA | 0.023 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.023 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E117 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFHxS | 6 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0980000 | NA | 0.034 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.034 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E118 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFOS | 8 | Yes | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 45.0900000 | NA | 3.709 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 3.709 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E119 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | PFDS | 10 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1780000 | NA | 0.094 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.094 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E120 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | 6:6PFPIA | 12 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0350000 | NA | 0.006 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.006 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E121 | Walleye | Sander vitreus | vertebrate | freshwater fish | 18.71 | 6:8PFPIA | 14 | No | Baking | oil-based | NA | 200 | 900 | No | Yes | canola oil | 11.000 | 11.000 | 0.0583152 | 188.6300 | C014 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0630000 | NA | 0.017 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.017 | 0.0583152 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E122 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFNA | 9 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0740000 | NA | 0.014 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.014 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E123 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDA | 10 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3380000 | NA | 0.098 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.098 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E124 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFUnDA | 11 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3480000 | NA | 0.102 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.102 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E125 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDoDA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1440000 | NA | 0.037 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.037 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E126 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFTrA | 13 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.2170000 | NA | 0.041 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.041 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E127 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFTA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0940000 | NA | 0.025 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.025 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E128 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFHxS | 6 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0880000 | NA | 0.036 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.036 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E129 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFOS | 8 | Yes | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 52.6900000 | NA | 14.62 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 14.62 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E130 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | PFDS | 10 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1890000 | NA | 0.08 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.08 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E131 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | 6:6PFPIA | 12 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0400000 | NA | 0.008 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.008 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E132 | Walleye | Sander vitreus | vertebrate | freshwater fish | 24.09 | 6:8PFPIA | 14 | No | Broiling | oil-based | NA | 300 | 600 | No | Yes | canola oil | 0.506 | 0.506 | 0.0028457 | 177.8100 | C015 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.012 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.012 | 0.0028457 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E133 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFNA | 9 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0630000 | NA | 0.0210000 | sd | biological | 5.000000 | 5 | 0.0670000 | NA | 0.015 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0210000 | 0.015 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E134 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDA | 10 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2480000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.2990000 | NA | 0.072 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.072 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E135 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFUnDA | 11 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2390000 | NA | 0.0400000 | sd | biological | 5.000000 | 5 | 0.3070000 | NA | 0.076 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0400000 | 0.076 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E136 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDoDA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1050000 | NA | 0.0190000 | sd | biological | 5.000000 | 5 | 0.1290000 | NA | 0.049 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0190000 | 0.049 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E137 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFTrA | 13 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1490000 | NA | 0.0200000 | sd | biological | 5.000000 | 5 | 0.1790000 | NA | 0.054 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0200000 | 0.054 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E138 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFTA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0690000 | NA | 0.0090000 | sd | biological | 5.000000 | 5 | 0.0870000 | NA | 0.034 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0090000 | 0.034 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E139 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFHxS | 6 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0800000 | NA | 0.0250000 | sd | biological | 5.000000 | 5 | 0.0830000 | NA | 0.027 | biological | NA | ng/g | Probably <0.006 | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0250000 | 0.027 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E140 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFOS | 8 | Yes | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 36.7900000 | NA | 1.6240000 | sd | biological | 5.000000 | 5 | 44.5100000 | NA | 7.718 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 1.6240000 | 7.718 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E141 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | PFDS | 10 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.1060000 | NA | 0.0240000 | sd | biological | 5.000000 | 5 | 0.1570000 | NA | 0.066 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0240000 | 0.066 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E142 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | 6:6PFPIA | 12 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0260000 | NA | 0.0060000 | sd | biological | 5.000000 | 5 | 0.0290000 | NA | 0.004 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0060000 | 0.004 | 0.0615832 | ||
| F003 | Bhavsar_2014 | 2014 | Canada | E143 | Walleye | Sander vitreus | vertebrate | freshwater fish | 14.45 | 6:8PFPIA | 14 | No | Frying | oil-based | NA | 175 | 600 | No | Yes | canola oil | 11.000 | 11.000 | 0.0615832 | 178.6200 | C016 | Shared control | NA | 5.000000 | 5 | ng/g | 0.0670000 | NA | 0.0100000 | sd | biological | 5.000000 | 5 | 0.0770000 | NA | 0.005 | biological | NA | ng/g | Not provided | Not provided | Dependent | Table S3 | No | We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. | 0.0100000 | 0.005 | 0.0615832 | ||
| F005 | DelGobbo_2008 | 2008 | Canada | E144 | Catfish | Ictalurus punctatus | vertebrate | freshwater fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C017 | Shared control | NA | 19.000000 | 1 | ng/g | 1.5657252 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.9000000 | NA | Not provided | technical | 4 | ng/g | 0.364605839 | 1.093817517 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | ML | NA | NA | 0.0625000 |
| F005 | DelGobbo_2008 | 2008 | Canada | E145 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | Shared control | NA | 14.000000 | 1 | ng/g | 1.3600000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0169896 | LOD | Not provided | technical | 4 | ng/g | 0.016989626 | 0.050968879 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E146 | Grouper | Epinephelus itajara | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C018 | Shared control | NA | 14.000000 | 1 | ng/g | 0.3715856 | LOD | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.4700000 | NA | Not provided | technical | 4 | ng/g | 0.371585648 | 1.114756944 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E147 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 0.0774969 | LOD | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.0600000 | NA | Not provided | technical | 4 | ng/g | 0.077496939 | 0.232490816 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E148 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.0032120 | LOD | Not provided | technical | 4 | ng/g | 0.003212028 | 0.009636084 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E149 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 0.0270203 | LOD | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.3900000 | NA | Not provided | technical | 4 | ng/g | 0.027020324 | 0.081060971 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E150 | Monkfish | Lophius americanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C019 | Shared control | NA | 9.000000 | 1 | ng/g | 1.3400000 | NA | NA | Not available because sample size is one. | technical | 9.000000 | 1 | 0.2200000 | NA | Not provided | technical | 4 | ng/g | 0.233373227 | 0.70011968 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E151 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 0.7800000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0600000 | NA | Not provided | technical | 3 | ng/g | 0.026125853 | 0.07837756 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E152 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.2900000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0261259 | LOD | Not provided | technical | 3 | ng/g | 0.026125853 | 0.07837756 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E153 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFDA | 10 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0120876 | LOD | Not provided | technical | 3 | ng/g | 0.012087592 | 0.036262776 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E154 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 1.8800000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 1.5900000 | NA | Not provided | technical | 3 | ng/g | 0.023403463 | 0.07021039 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E155 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 2.6100000 | NA | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.0071943 | LOD | Not provided | technical | 3 | ng/g | 0.007194278 | 0.021582834 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E156 | Octopus | Bathypolypus arcticus | invertebrate | mollusca | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C020 | Shared control | NA | 15.000000 | 1 | ng/g | 0.5086163 | LOD | NA | Not available because sample size is one. | technical | 15.000000 | 1 | 0.2300000 | NA | Not provided | technical | 3 | ng/g | 0.508616305 | 1.525848915 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E157 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C021 | Shared control | NA | 19.000000 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.2100000 | NA | Not provided | technical | 4 | ng/g | 0.335745729 | 1.007237187 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E158 | Red snapper | Lutjanus campechanus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C021 | Shared control | NA | 19.000000 | 1 | ng/g | 1.4600000 | NA | NA | Not available because sample size is one. | technical | 19.000000 | 1 | 0.7800000 | NA | Not provided | technical | 4 | ng/g | 0.212707733 | 0.6381232 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E159 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | Shared control | NA | 22.000000 | 1 | ng/g | 1.5800000 | NA | NA | Not available because sample size is one. | technical | 22.000000 | 1 | 1.5900000 | NA | Not provided | technical | 3 | ng/g | 0.030799263 | 0.092397789 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E160 | Sea squirt | Diplosoma listerianum | vertebrate | tunicata | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C022 | Shared control | NA | 22.000000 | 1 | ng/g | 1.3200000 | NA | NA | Not available because sample size is one. | technical | 22.000000 | 1 | 0.9600000 | NA | Not provided | technical | 3 | ng/g | 0.00466163 | 0.013984889 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E161 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFNA | 9 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.0900000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0027709 | LOD | Not provided | technical | 4 | ng/g | 0.002770915 | 0.008312745 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E162 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.5500000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 1.3500000 | NA | Not provided | technical | 4 | ng/g | 0.012033653 | 0.03610096 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E163 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFDoDA | 12 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.3300000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0255728 | LOD | Not provided | technical | 4 | ng/g | 0.025572815 | 0.076718446 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E164 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFTA | 14 | NA | No | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 0.6700000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.0070174 | LOD | Not provided | technical | 4 | ng/g | 0.00701744 | 0.021052319 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E165 | Skate | Amblyraja hyperborea | vertebrate | rays | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C023 | Shared control | NA | 14.000000 | 1 | ng/g | 1.5100000 | NA | NA | Not available because sample size is one. | technical | 14.000000 | 1 | 0.8800000 | NA | Not provided | technical | 4 | ng/g | 0.364216663 | 1.092649988 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E166 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | Shared control | NA | 35.000000 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.0179042 | LOD | Not provided | technical | 4 | ng/g | 0.017904207 | 0.05371262 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E167 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 420 | Yes | No | NA | NA | NA | 30.0000000 | NA | C024 | Shared control | NA | 35.000000 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.8900000 | NA | Not provided | technical | 4 | ng/g | 0.376885418 | 1.130656253 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 30.0000000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E168 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | Shared control | NA | 35.000000 | 1 | ng/g | 1.5700000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 2.1100000 | NA | Not provided | technical | 4 | ng/g | 0.016586028 | 0.049758083 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F005 | DelGobbo_2008 | 2008 | Canada | E169 | Yellow croaker | Larimichthys polyactis | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 163 | 900 | No | Yes | sesame oil | NA | NA | 0.0625000 | NA | C025 | Shared control | NA | 35.000000 | 1 | ng/g | 1.6800000 | NA | NA | Not available because sample size is one. | technical | 35.000000 | 1 | 0.6800000 | NA | Not provided | technical | 4 | ng/g | 0.392175529 | 1.176526586 | Dependent | Table 3 | Yes | Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species | NA | NA | 0.0625000 | |
| F006 | Hu_2020 | 2020 | China | E170 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFBA | 3 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 5.3412073 | NA | 1.688925302 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | ML | 7.4193907 | 1.688925302 | NA | |
| F006 | Hu_2020 | 2020 | China | E171 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFOA | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.2674068 | NA | 0.08 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.08 | NA | ||
| F006 | Hu_2020 | 2020 | China | E172 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFBS | 4 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 23.9801208 | NA | 26.845 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 26.845 | NA | ||
| F006 | Hu_2020 | 2020 | China | E173 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFOS | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 122.4133110 | NA | 62.469 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 62.469 | NA | ||
| F006 | Hu_2020 | 2020 | China | E174 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFHpA | 7 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 55.3995680 | NA | 55.4 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 55.4 | NA | ||
| F006 | Hu_2020 | 2020 | China | E175 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFDoDA | 12 | No | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.2676991 | NA | 1.533 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 1.533 | NA | ||
| F006 | Hu_2020 | 2020 | China | E176 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | PFHxS | 6 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.8685897 | NA | 0.303 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.303 | NA | ||
| F006 | Hu_2020 | 2020 | China | E177 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 14.38 | FOSA | 8 | Yes | Steaming | water-based | on a stainless-steel plate in a steamer | 100 | 480 | Yes | No | NA | NA | NA | NA | 70.0000 | C026 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.3838798 | NA | 1.290418258 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 1.290418258 | NA | ||
| F006 | Hu_2020 | 2020 | China | E178 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFBA | 3 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 4.9146982 | NA | 7.434 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 7.434 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E179 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFOA | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.1932566 | NA | 0.071 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.071 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E180 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFBS | 4 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 10.8230680 | NA | 7.461 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 7.461 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E181 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFOS | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 97.7348993 | NA | 23.173 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 23.173 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E182 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFHpA | 7 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 13.7149028 | NA | 23.604 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 23.604 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E183 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFDoDA | 12 | No | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.3534292 | NA | 2.484 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 2.484 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E184 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | PFHxS | 6 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.6506410 | NA | 0.108 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.108 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E185 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 16.48 | FOSA | 8 | Yes | Boiling | water-based | NA | 100 | 120 | Yes | No | NA | 300.000 | 300.000 | 4.2857143 | 70.0000 | C027 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.2540984 | NA | 1.248 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 1.248 | 4.2857143 | ||
| F006 | Hu_2020 | 2020 | China | E186 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFBA | 3 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 7.9068241 | NA | 9.381 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 9.381 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E187 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFOA | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.2308114 | NA | 0.154 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.154 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E188 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFBS | 4 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 9.8657220 | NA | 5.801 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 5.801 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E189 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFOS | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 134.4379195 | NA | 58.054 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 58.054 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E190 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFHpA | 7 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 23.7041037 | NA | 35.93 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 35.93 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E191 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFDoDA | 12 | No | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.8733407 | NA | 2.747 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 2.747 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E192 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | PFHxS | 6 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 1.1602564 | NA | 0.738 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.738 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E193 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 23.14 | FOSA | 8 | Yes | Frying | oil-based | In a stainless-steel pan | 180 | 150 | No | Yes | Not specified | 100.000 | 100.000 | 1.4285714 | 70.0000 | C028 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 3.7500000 | NA | 3.741 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 3.741 | 1.4285714 | ||
| F006 | Hu_2020 | 2020 | China | E194 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFBA | 3 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 6.9619423 | NA | 7.4193907 | sd | biological | 5.000000 | 5 | 4.8490814 | NA | 6.93 | biological | Not specified | ng/g | Not provided | 12.2 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 7.4193907 | 6.93 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E195 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFOA | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 0.2098410 | NA | 0.1560332 | sd | biological | 5.000000 | 5 | 0.1652961 | NA | 0.063 | biological | Not specified | ng/g | Not provided | 0.226 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.1560332 | 0.063 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E196 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFBS | 4 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 24.8753463 | NA | 23.9889753 | sd | biological | 5.000000 | 5 | 7.5376305 | NA | 1.502 | biological | Not specified | ng/g | Not provided | 1.01 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 23.9889753 | 1.502 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E197 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFOS | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 86.6890380 | NA | 39.4592027 | sd | biological | 5.000000 | 5 | 121.7142058 | NA | 62.557 | biological | Not specified | ng/g | Not provided | 1.57 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 39.4592027 | 62.557 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E198 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFHpA | 7 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 24.2980562 | NA | 30.6129835 | sd | biological | 5.000000 | 5 | 10.0971922 | NA | 16.49 | biological | Not specified | ng/g | Not provided | 0.47 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 30.6129835 | 16.49 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E199 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFDoDA | 12 | No | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 1.5680310 | NA | 0.5599538 | sd | biological | 5.000000 | 5 | 2.9120575 | NA | 3.36 | biological | Not specified | ng/g | Not provided | 0.093 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 0.5599538 | 3.36 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E200 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | PFHxS | 6 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 1.8092949 | NA | 2.3827419 | sd | biological | 5.000000 | 5 | 0.8253205 | NA | 0.254 | biological | Not specified | ng/g | Not provided | 0.155 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 2.3827419 | 0.254 | 0.1428571 | ||
| F006 | Hu_2020 | 2020 | China | E201 | Grass carp | Ctenopharyngodon idell | vertebrate | freshwater fish | 21.31 | FOSA | 8 | Yes | Grilling | oil-based | Domestic electric oven set to broil | 210 | 600 | No | Yes | Not specified | 10.000 | 10.000 | 0.1428571 | 70.0000 | C029 | Shared control | NA | 5.000000 | 5 | ng/g | 2.5990437 | NA | 1.6889253 | sd | biological | 5.000000 | 5 | 2.2814208 | NA | 0.43 | biological | Not specified | ng/g | Not provided | 0.026 | Dependent | Figure 3 | No | We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. | 1.6889253 | 0.43 | 0.1428571 | ||
| F007 | Kim_2020 | 2020 | Korea | E202 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. | ML | NA | NA | 0.0500000 | |
| F007 | Kim_2020 | 2020 | Korea | E203 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1100000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E204 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | NA | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E205 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFOA | 8 | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.06 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E206 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E207 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1300000 | NA | 0.04 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E208 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.1400000 | NA | 0.01 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E209 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBA | 3 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1600000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E210 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E211 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E212 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E213 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFHpA | 7 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0700000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E214 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E215 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E216 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E217 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFDoDA | 12 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0200000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E218 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0500000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E219 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0.02 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E220 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E221 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFTrA | 13 | No | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.0800000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0 | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E222 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Grilling | oil-based | NA | NA | 360 | No | Yes | Not specified | 5.000 | 5.000 | 0.0500000 | 100.0000 | C030 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 0.0500000 | ||
| F007 | Kim_2020 | 2020 | Korea | E223 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Braising | water-based | NA | 100 | 1500 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C031 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E224 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Steaming | water-based | NA | 100 | 900 | Yes | No | NA | 250.000 | 250.000 | 2.5000000 | 100.0000 | C032 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 2.5000000 | ||
| F007 | Kim_2020 | 2020 | Korea | E225 | Mackerel | Scomber japonicus | vertebrate | marine fish | NA | PFBS | 4 | Yes | Frying | oil-based | NA | 160 | 300 | No | Yes | Not specified | 750.000 | 750.000 | 7.5000000 | 100.0000 | C033 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1900000 | NA | 0.0100000 | sd | technical | 10.000000 | 1 | 0.0200000 | LOD | NA | technical | Not specified | ng/g | 0.02 to 0.09 | 0.08 to 0.27 | Dependent | Table 2 | No | We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. | NA | NA | 7.5000000 | ||
| F008 | Luo_2019 | 2019 | Korea | E316 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOA | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 20.7900000 | NA | 0.1700000 | sd | technical | 5.000000 | 1 | 16.7700000 | NA | 0.42 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) | NA | NA | 2.5000000 | |||
| F008 | Luo_2019 | 2019 | Korea | E317 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFOS | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.8100000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.7400000 | NA | 0.03 | technical | ng/g | 0.07 | 0.07 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E318 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFBA | 3 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.1400000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.0400000 | NA | 0.01 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E319 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHpA | 7 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.3700000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.3200000 | NA | 0.01 | technical | ng/g | 0.06 | 0.17 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E320 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFNA | 9 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 2.8900000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 2.3000000 | NA | 0.03 | technical | ng/g | 0.03 | 0.08 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E321 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDA | 10 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6600000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.5700000 | NA | 0.02 | technical | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E322 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFUnDA | 11 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.9300000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.7900000 | NA | 0.02 | technical | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E323 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDoDA | 12 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.2500000 | NA | 0.0200000 | sd | technical | 5.000000 | 1 | 0.2300000 | NA | 0.01 | technical | ng/g | 0.06 | 0.19 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E324 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTrA | 13 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 1.1200000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 1.3800000 | NA | 0.09 | technical | ng/g | 0.05 | 0.16 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E325 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFTA | 14 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.2800000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.2600000 | NA | 0.02 | technical | ng/g | 0.05 | 0.15 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E326 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFHxS | 6 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.4800000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.3300000 | NA | 0.03 | technical | ng/g | 0.08 | 0.25 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E327 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | PFDS | 10 | No | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 0.0400000 | NA | 0.0100000 | sd | technical | 5.000000 | 1 | 0.0400000 | NA | 0.01 | technical | ng/g | 0.09 | 0.27 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F008 | Luo_2019 | 2019 | Korea | E328 | Swimming crab | Portunus trituberculatus | invertebrate | crustacea | NA | FOSA | 8 | Yes | Boiling | water-based | Boiled with radish | 100 | 600 | Yes | No | NA | 2500.000 | 2500.000 | 2.5000000 | 1000.0000 | C040 | Shared control | NA | 5.000000 | 1 | ng/g | 1.5400000 | NA | 0.0900000 | sd | technical | 5.000000 | 1 | 2.5500000 | NA | 0.19 | technical | ng/g | 0.04 | 0.11 | Dependent | Table 4 | No | NA | NA | 2.5000000 | ||||
| F010 | Sungur_2019 | 2019 | Turkey | E329 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1590000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | ML - note shared controls for differend cooking times and methods | NA | NA | 30.0000000 |
| F010 | Sungur_2019 | 2019 | Turkey | E330 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1170000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E331 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0790000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E332 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E333 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1160000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E334 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E335 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1400000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E336 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1330000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E337 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0710000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E338 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2010000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E339 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0590000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E340 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2320000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0480000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E341 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C041 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 14.7000000 | NA | 0.009 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E342 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C042 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 9.3500000 | NA | 0.008 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E343 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C043 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.6600000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E344 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C044 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 5.6300000 | NA | 0.005 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E345 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C045 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 4.5000000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E346 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C046 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.7700000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E347 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C047 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 8.2800000 | NA | 0.007 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E348 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C048 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 6.6200000 | NA | 0.006 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E349 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C049 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.4800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E350 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C050 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 4.4900000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E351 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C051 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 3.0500000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E352 | Bluefish | Pomatomus saltator | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C052 | Shared control | NA | 10.000000 | 1 | ng/g | 24.0000000 | NA | 0.0110000 | sd | technical | 10.000000 | 1 | 2.8300000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E353 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E354 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1180000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E355 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0840000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E356 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2030000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E357 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1390000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E358 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1040000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E359 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2070000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E360 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E361 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E362 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E363 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0510000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E364 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2140000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2550000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E365 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C053 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 4.7800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E366 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C054 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 3.5000000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E367 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C055 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.5100000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E368 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C056 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 7.0500000 | NA | 0.006 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E369 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C057 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.4700000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E370 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C058 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.7600000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E371 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C059 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 3.0300000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E372 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C060 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.0400000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E373 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C061 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.2300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E374 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C062 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 4.2800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E375 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C063 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 2.7800000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E376 | Red mullet | Mullus barbatus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C064 | Shared control | NA | 10.000000 | 1 | ng/g | 8.9200000 | NA | 0.0070000 | sd | technical | 10.000000 | 1 | 1.0200000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E377 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E378 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1870000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E379 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E380 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1750000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E381 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1530000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E382 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E383 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1890000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E384 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1320000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E385 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0930000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E386 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1810000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E387 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0880000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E388 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2550000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0660000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E389 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C065 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.1500000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E390 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C066 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 2.6500000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E391 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C067 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.2300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E392 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C068 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.4400000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E393 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C069 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 2.3600000 | NA | 0.003 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E394 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C070 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.6500000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E395 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C071 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 3.6800000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E396 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C072 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.7300000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E397 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C073 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 0.9200000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E398 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C074 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 4.0300000 | NA | 0.004 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E399 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C075 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 1.9700000 | NA | 0.002 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E400 | Whitefish | Salmo trutta | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C076 | Shared control | NA | 10.000000 | 1 | ng/g | 5.0700000 | NA | 0.0040000 | sd | technical | 10.000000 | 1 | 0.8400000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E401 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2020000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E402 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E403 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E404 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1580000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E405 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1210000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E406 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E407 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1680000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E408 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1340000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E409 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0910000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E410 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1740000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E411 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0960000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E412 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2380000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0440000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E413 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C077 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2760000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E414 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C078 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1750000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E415 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C079 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E416 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C080 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3110000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E417 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C081 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2840000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E418 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C082 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E419 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C083 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E420 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C084 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1610000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E421 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C085 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0850000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E422 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C086 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1640000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E423 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C087 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0930000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E424 | Common pandora | Pagellus erythrinus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C088 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4070000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0670000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E425 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1970000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E426 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1460000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E427 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0900000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E428 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2120000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E429 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1220000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E430 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E431 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E432 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E433 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0690000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E434 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E435 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E436 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2980000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0420000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E437 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C089 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3720000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E438 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C090 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2510000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E439 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C091 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E440 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C092 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E441 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C093 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1800000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E442 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C094 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E443 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C095 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3260000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E444 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C096 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1550000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E445 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C097 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0630000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E446 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C098 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3580000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E447 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C099 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E448 | Flathead grey mullet | Mugil cephalus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C100 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4180000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0560000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E449 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E450 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1150000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E451 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0500000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E452 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1480000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E453 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1070000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E454 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | oil-based | NA | 160 | 1200 | No | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C106 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0570000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E455 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1210000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E456 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0950000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E457 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0430000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E458 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1150000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E459 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E460 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1530000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0330000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E461 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C101 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6640000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E462 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C102 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3120000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E463 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C103 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E464 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C104 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6180000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E465 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C105 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3780000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E466 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C106 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1070000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E467 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C107 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.5980000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E468 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C108 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.4020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E469 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C109 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0970000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E470 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C110 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.6180000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E471 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C111 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2460000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E472 | Atlantic mackerel | Scomber scombrus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C112 | Shared control | NA | 10.000000 | 1 | ng/g | 0.7860000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0890000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E473 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E474 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0620000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E475 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0430000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E476 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0800000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E477 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0600000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E478 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E479 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0980000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E480 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0700000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E481 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0340000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E482 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0650000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E483 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0580000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E484 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1080000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0320000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E485 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C113 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E486 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C114 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1080000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E487 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C115 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E488 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C116 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1470000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E489 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C117 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E490 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C118 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0940000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E491 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C119 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1260000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E492 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C120 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E493 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C121 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0520000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E494 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C122 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1020000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E495 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C123 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0760000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E496 | Pike-perch | Dicentrarchus labrax | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C124 | Shared control | NA | 10.000000 | 1 | ng/g | 0.2740000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0490000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E497 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1450000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E498 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1130000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E499 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0540000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E500 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1520000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E501 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1280000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E502 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0610000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E503 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1220000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E504 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E505 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0490000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E506 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1180000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E507 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0890000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E508 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOA | 8 | NA | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | Shared control | NA | 10.000000 | 1 | ng/g | 0.1810000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0440000 | NA | 0.001 | technical | 3 | ng/g | 0.009 | 0.03 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E509 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 600 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C125 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3570000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E510 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 900 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C126 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2100000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E511 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Boiling | water-based | NA | 100 | 1200 | Yes | No | NA | 300.000 | 300.000 | 30.0000000 | 10.0000 | C127 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E512 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 600 | No | No | NA | NA | 0.000 | NA | NA | C128 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.2560000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E513 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 900 | No | No | NA | NA | 0.000 | NA | NA | C129 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1840000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E514 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Baking | No liquid | NA | 160 | 1200 | No | No | NA | NA | 0.000 | NA | NA | C130 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0990000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 0.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E515 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 600 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C131 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3440000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E516 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 900 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C132 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1480000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E517 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in olive oil | 160 | 1200 | No | Yes | olive oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C133 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0820000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E518 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 600 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C134 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.3410000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E519 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 900 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C135 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.1920000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F010 | Sungur_2019 | 2019 | Turkey | E520 | Mediterranean sand smelt | Atherina hepsetus | vertebrate | marine fish | NA | PFOS | 8 | linear | Yes | Frying | oil-based | in sunflower oil | 160 | 1200 | No | Yes | sunflower oil | 300.000 | 300.000 | 30.0000000 | 10.0000 | C136 | Shared control | NA | 10.000000 | 1 | ng/g | 0.4760000 | NA | 0.0010000 | sd | technical | 10.000000 | 1 | 0.0540000 | NA | 0.001 | technical | 3 | ng/g | 0.006 | 0.02 | Dependent | Table 3 | No | Authors replied | NA | NA | 30.0000000 | |
| F011 | Taylor_2019 | 2019 | Australia | E521 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFHxS | 6 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 4.000000 | 4 | ng/g | 0.9673000 | NA | 1.0026000 | sd | biological | 4.000000 | 4 | 1.4750000 | NA | 1.743 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | ML - check empty fields, why SE/SD field is NA? | 1.0026000 | 1.743 | 0.3976934 |
| F011 | Taylor_2019 | 2019 | Australia | E522 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6.000000 | 6 | 84.5500000 | NA | 130.5 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 133.7000000 | 130.5 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E523 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFOS | 8 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.4610420 | 43.3800 | C138 | Shared control | Clean site | 3.000000 | 3 | ng/g | 0.0894000 | NA | 0.0339000 | sd | biological | 3.000000 | 3 | 0.1210000 | NA | 0.039 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0339000 | 0.039 | 0.4610420 | |
| F011 | Taylor_2019 | 2019 | Australia | E526 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | PFDS | 10 | linear | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 2.000000 | 2 | ng/g | 0.1391000 | NA | 0.0247000 | sd | biological | 2.000000 | 2 | 0.3760000 | NA | 0.024 | biological | 1 | ng/g | 0.030122517 | 0.10040839 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0247000 | 0.024 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E527 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 21.47 | FOSA | 8 | NA | Yes | Baking | oil-based | NA | 75 | 600 | No | Yes | olive oil | 20.000 | 20.000 | 0.3976934 | 50.2900 | C137 | Shared control | Contaminated site | 2.000000 | 2 | ng/g | 0.0749000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 2.000000 | 2 | 0.1990000 | NA | 0.012 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | 0.012 | 0.3976934 | |
| F011 | Taylor_2019 | 2019 | Australia | E528 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFHxS | 6 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C140 | Shared control | Contaminated site | 5.000000 | 5 | ng/g | 0.7841000 | NA | 0.9602000 | sd | biological | 5.000000 | 5 | 0.8410000 | NA | 1.042 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.9602000 | 1.042 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E529 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Shared control | Contaminated site | 6.000000 | 6 | ng/g | 75.6360000 | NA | 133.7000000 | sd | biological | 6.000000 | 6 | 70.8430000 | NA | 106 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 133.7000000 | 106 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E530 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.9220839 | 43.3800 | C140 | Shared control | Clean site | 2.000000 | 2 | ng/g | 0.1090000 | NA | 0.0014000 | sd | biological | 2.000000 | 2 | 0.2010000 | NA | 0.073 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0014000 | 0.073 | 0.9220839 | |
| F011 | Taylor_2019 | 2019 | Australia | E533 | Dusky flathead | Platycephalus fuscus | vertebrate | marine fish | 18.64 | FOSA | 8 | NA | Yes | Frying | oil-based | NA | 82 | 120 | No | Yes | olive oil | 40.000 | 40.000 | 0.7696748 | 51.9700 | C139 | Shared control | Contaminated site | 4.000000 | 4 | ng/g | 0.1070000 | NA | 0.0397000 | sd | biological | 4.000000 | 4 | 0.2540000 | NA | 0.132 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0397000 | 0.132 | 0.7696748 | |
| F011 | Taylor_2019 | 2019 | Australia | E534 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxA | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 3.000000 | 3 | ng/g | 0.1513000 | NA | 0.0306000 | sd | biological | 3.000000 | 3 | 0.0730000 | NA | 0.021 | biological | 1 | ng/g | 0.028099467 | 0.093664888 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0306000 | 0.021 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E535 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHpA | 7 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.2070000 | NA | 0.1445000 | sd | biological | 6.000000 | 6 | 0.1090000 | NA | 0.052 | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.1445000 | 0.052 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E536 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.4279000 | NA | 0.2601000 | sd | biological | 6.000000 | 6 | 0.2320000 | NA | 0.107 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2601000 | 0.107 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E537 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 4.000000 | 4 | ng/g | 0.0433000 | NA | 0.0137000 | sd | biological | 4.000000 | 4 | 0.0710000 | NA | 0.066 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0137000 | 0.066 | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E538 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 4.000000 | 4 | ng/g | 0.1128000 | NA | 0.0093000 | sd | biological | 4.000000 | 4 | 0.0580000 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.0093000 | NA | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E539 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFUnDA | 11 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1047000 | NA | NA | sd | biological | 1.000000 | 1 | 0.0580000 | <LOQ | NA | NA | 1 | ng/g | 0.026755217 | 0.089184057 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E540 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 1.000000 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1.000000 | 1 | 0.1280000 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E541 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1230000 | NA | NA | sd | biological | 1.000000 | 1 | 0.0800000 | <LOQ | NA | NA | 1 | ng/g | 0.037026547 | 0.123421824 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E542 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.5991000 | NA | 0.2053000 | sd | biological | 6.000000 | 6 | 0.3870000 | NA | 0.079 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2053000 | 0.079 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E543 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFHxS | 6 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 1.000000 | 1 | ng/g | 0.1230000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 1.000000 | 1 | 0.0810000 | No sd, as N = 1 | NA | NA | 1 | ng/g | 0.023508736 | 0.078362453 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | NA | NA | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E544 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 5.0500000 | NA | 0.4637000 | sd | biological | 6.000000 | 6 | 5.5330000 | NA | 0.829 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.4637000 | 0.829 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E545 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | PFOS | 8 | linear | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 40.4858300 | 12.3500 | C142 | NA | Clean site | 6.000000 | 6 | ng/g | 0.1917000 | NA | 0.2129000 | sd | biological | 6.000000 | 6 | 0.1920000 | NA | 0.236 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.2129000 | 0.236 | 40.4858300 | |
| F011 | Taylor_2019 | 2019 | Australia | E548 | Blue swimmer crab | Portunus armatus | invertebrate | crustacea | NA | FOSA | 8 | NA | No | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 420 | Yes | No | NA | 500.000 | 500.000 | 45.3309157 | 11.0300 | C141 | NA | Contaminated site | 6.000000 | 6 | ng/g | 0.3112000 | NA | 0.1413000 | sd | biological | 6.000000 | 6 | 0.3220000 | NA | 0.099 | biological | 1 | ng/g | 0.034582913 | 0.115276378 | Dependent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied | 0.1413000 | 0.099 | 45.3309157 | |
| F011 | Taylor_2019 | 2019 | Australia | E549 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHpA | 7 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0802000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.1280000 | NA | NA | biological | 1 | ng/g | 0.01867491 | 0.0622497 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | NA | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E550 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.2229000 | NA | 0.0668000 | sd | biological | 10.000000 | 1 | 0.4690000 | NA | 0.104 | biological | 1 | ng/g | 0.014519809 | 0.048399364 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 0.0668000 | 0.104 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E551 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0910000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.2330000 | NA | 0.037 | biological | 1 | ng/g | 0.036013573 | 0.120045244 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | 0.037 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E552 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 0.0854000 | <LOQ | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 10.000000 | 1 | 0.1880000 | NA | 0.053 | biological | 1 | ng/g | 0.039417906 | 0.131393021 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | NA | 0.053 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E553 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFHxS | 6 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 2.3305000 | NA | 1.3905000 | sd | biological | 10.000000 | 1 | 6.3160000 | NA | 1.628 | biological | 1 | ng/g | 0.023508736 | 0.078362453 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 1.3905000 | 1.628 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E554 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 7.7375426 | 64.6200 | C143 | NA | Contaminated site | 10.000000 | 1 | ng/g | 7.4167000 | NA | 2.8414000 | sd | biological | 10.000000 | 1 | 16.1670000 | NA | 3.869 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 2.8414000 | 3.869 | 7.7375426 | |
| F011 | Taylor_2019 | 2019 | Australia | E555 | School prawn | Metapenaeus macleayi | invertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Boiling | water-based | in saltwater (8.5 g/L) | 100 | 240 | Yes | No | NA | 500.000 | 500.000 | 12.5376128 | 39.8800 | C144 | NA | Clean site | 10.000000 | 1 | ng/g | 0.0560000 | NA | 0.0133000 | sd | biological | 10.000000 | 1 | 0.1180000 | NA | 0.029 | biological | 1 | ng/g | 0.023185477 | 0.077284922 | Independent | Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota | Yes | Authors replied, shrimp sample sizes in paper at 10 per group, we used sample sizes as reported in the main text (conservative sample sizes), although the actual PFAS concentrations were from the raw data provided by the author. | 0.0133000 | 0.029 | 12.5376128 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E613 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 7.666667 | 1 | 1.7500000 | NA | 0.05 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods | NA | NA | 0.9000000 |
| F013 | Vassiliadou_2015 | 2015 | Greece | E558 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 7.666667 | 1 | 2.9900000 | NA | 0.22 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.9000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E559 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 72.74 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.9000000 | 333.3333 | C145 | Shared control | NA | 7.666667 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 7.666667 | 1 | 6.6200000 | NA | 0.14 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.9000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E560 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 4.000000 | 1 | 0.4400000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E561 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 4.000000 | 1 | 1.1200000 | NA | 0.03 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E562 | Bogue | Boops boops | vertebrate | marine fish | 18.35 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3600000 | 833.3333 | C146 | Shared control | NA | 4.000000 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 4.000000 | 1 | 1.2700000 | NA | 0.06 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3600000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E563 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 6.666667 | 1 | 0.7000000 | LOD | NA | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E564 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 6.666667 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E565 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 6.666667 | 1 | 0.8300000 | NA | 0.03 | technical | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E566 | Hake | Merluccius merluccius | vertebrate | marine fish | 36.00 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4300000 | 697.6744 | C147 | Shared control | NA | 6.666667 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 6.666667 | 1 | 1.2400000 | NA | 0.06 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4300000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E567 | Picarel | Spicara smaris | vertebrate | marine fish | 44.04 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | Shared control | NA | 6.666667 | 1 | ng/g | 0.7000000 | NA | 0.0900000 | sd | technical | 6.666667 | 1 | 1.3500000 | NA | 0.08 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E568 | Picarel | Spicara smaris | vertebrate | marine fish | 44.04 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4700000 | 638.2979 | C148 | Shared control | NA | 6.666667 | 1 | ng/g | 20.3700000 | NA | 2.4700000 | sd | technical | 6.666667 | 1 | 44.6900000 | NA | 3.93 | technical | 1 | ng/g | 0.49 | 1.48 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E569 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 0.3500000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 13.000000 | 1 | 0.7400000 | NA | 0.09 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E570 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 1.0800000 | NA | 0.0300000 | sd | technical | 13.000000 | 1 | 1.9800000 | NA | 0.04 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E571 | Sand smelt | Atherina boyeri | vertebrate | marine fish | 79.11 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5200000 | 576.9231 | C149 | Shared control | NA | 13.000000 | 1 | ng/g | 1.1600000 | NA | 0.0500000 | sd | technical | 13.000000 | 1 | 3.0100000 | NA | 0.13 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5200000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E572 | Sardine | Sardina pilchardus | vertebrate | marine fish | 57.26 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.8800000 | 340.9091 | C150 | Shared control | NA | 4.666667 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 4.666667 | 1 | 0.9300000 | NA | 0.03 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.8800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E573 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.5700000 | NA | 0.11 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E574 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 0.5600000 | NA | 0.07 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E575 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 5.000000 | 1 | 0.7300000 | NA | 0.2 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E576 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 5.000000 | 1 | 1.3800000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E577 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 61.32 | PFOS | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.5700000 | 526.3158 | C151 | Shared control | NA | 5.000000 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 5.000000 | 1 | 0.1000000 | <LOD | NA | NA | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.5700000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E578 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 4.9400000 | NA | 0.2600000 | sd | technical | 10.000000 | 1 | 14.8800000 | NA | 1.61 | technical | 1 | ng/g | 0.39 | 1.17 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E579 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOA | 8 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | technical | 10.000000 | 1 | 0.9900000 | NA | 0.21 | technical | 1 | ng/g | 0.6 | 1.82 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E580 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFNA | 9 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.2700000 | NA | 0.0700000 | sd | technical | 10.000000 | 1 | 1.5200000 | NA | 0.11 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E581 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.7300000 | NA | 0.0800000 | sd | technical | 10.000000 | 1 | 1.8100000 | NA | 0.19 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E582 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 2.7600000 | NA | 0.2100000 | sd | technical | 10.000000 | 1 | 6.8200000 | NA | 0.22 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E583 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.3600000 | NA | 0.0900000 | sd | technical | 10.000000 | 1 | 2.3100000 | NA | 0.09 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E584 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFBS | 4 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 1.3700000 | NA | 0.1600000 | sd | technical | 10.000000 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E585 | Shrimp | Parapenaeus longirostris | vertebrate | crustacea | NA | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.4800000 | 625.0000 | C152 | Shared control | NA | 10.000000 | 1 | ng/g | 5.1500000 | NA | 0.3900000 | sd | technical | 10.000000 | 1 | 8.0200000 | NA | 0.42 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.4800000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E586 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFPeA | 5 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1950000 | <LOD | NA | sd | technical | 6.000000 | 1 | 5.0600000 | NA | 0.19 | technical | 1 | ng/g | 0.39 | 1.17 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E587 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFDA | 10 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 6.000000 | 1 | 0.5100000 | NA | 0.04 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E588 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFUnDA | 11 | NA | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.0400000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E589 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFDoDA | 12 | NA | No | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.000000 | 1 | 1.6500000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E590 | Squid | Loligo vulgaris | vertebrate | mollusca | 47.87 | PFOS | 8 | linear | Yes | Frying | oil-based | NA | 170 | NA | No | Yes | olive oil | 300.000 | 300.000 | 0.3900000 | 769.2308 | C153 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.5600000 | NA | 0.17 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.3900000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E591 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 0.3450000 | <LOD | NA | sd | technical | 13.000000 | 1 | 0.8300000 | NA | 0.01 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E592 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 1.5000000 | NA | 0.0400000 | sd | technical | 13.000000 | 1 | 2.7300000 | NA | 0.13 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E593 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 1.8600000 | NA | 0.1900000 | sd | technical | 13.000000 | 1 | 3.5200000 | NA | 0.1 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E594 | Anchovy | Engraulis encrasicolus | vertebrate | marine fish | 33.16 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C154 | Shared control | NA | 13.000000 | 1 | ng/g | 3.0600000 | NA | 0.1000000 | sd | technical | 13.000000 | 1 | 6.2900000 | NA | 0.34 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E595 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.2400000 | NA | 0.0300000 | sd | technical | 4.000000 | 1 | 0.4300000 | NA | 0.03 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E596 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.5600000 | NA | 0.0800000 | sd | technical | 4.000000 | 1 | 0.6300000 | NA | 0.02 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E597 | Bogue | Boops boops | vertebrate | marine fish | 7.44 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C155 | Shared control | NA | 4.000000 | 1 | ng/g | 0.8200000 | NA | 0.0400000 | sd | technical | 4.000000 | 1 | 0.8700000 | NA | 0.07 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E598 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.666667 | 1 | 0.8200000 | NA | 0.03 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E599 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4200000 | NA | 0.0500000 | sd | technical | 6.666667 | 1 | 1.1100000 | NA | 0.15 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E600 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.6200000 | NA | 0.0800000 | sd | technical | 6.666667 | 1 | 1.8900000 | NA | 0.05 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E601 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFBS | 4 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.4500000 | NA | 0.0700000 | sd | technical | 6.666667 | 1 | 0.2850000 | <LOD | NA | NA | 1 | ng/g | 0.57 | 1.7 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E602 | Hake | Merluccius merluccius | vertebrate | marine fish | 18.91 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C156 | Shared control | NA | 6.666667 | 1 | ng/g | 0.8400000 | NA | 0.1000000 | sd | technical | 6.666667 | 1 | 2.4000000 | NA | 0.13 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E603 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.3450000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 4.666667 | 1 | 0.8700000 | NA | 0.03 | technical | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E604 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.3500000 | <LOD | NA | sd | technical | 4.666667 | 1 | 1.7000000 | NA | 0.13 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E605 | Sardine | Sardina pilchardus | vertebrate | marine fish | 9.95 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C157 | Shared control | NA | 4.666667 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 4.666667 | 1 | 3.1900000 | NA | 0.09 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E606 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFNA | 9 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6000000 | NA | 0.0300000 | sd | technical | 5.000000 | 1 | 0.5000000 | NA | 0.05 | technical | 1 | ng/g | 0.42 | 1.25 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E607 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFDA | 10 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 0.6500000 | NA | 0.0600000 | sd | technical | 5.000000 | 1 | 0.3450000 | <LOD | NA | NA | 1 | ng/g | 0.69 | 2.08 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E608 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFUnDA | 11 | NA | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 1.0500000 | NA | 0.1300000 | sd | technical | 5.000000 | 1 | 0.8200000 | NA | 0.02 | technical | 1 | ng/g | 0.7 | 2.11 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E609 | Striped mullet | Mullus barbatus | vertebrate | marine fish | 17.66 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C158 | Shared control | NA | 5.000000 | 1 | ng/g | 5.6600000 | NA | 0.1500000 | sd | technical | 5.000000 | 1 | 10.2300000 | NA | 0.53 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E610 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFOA | 8 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.3000000 | <LOD | NA | Not available bacause Mc/Me is below LOD/LOQ | NA | 6.000000 | 1 | 0.4000000 | NA | 0.01 | technical | 1 | ng/g | 0.6 | 1.82 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E611 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFDoDA | 12 | NA | No | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.0900000 | NA | 0.02 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 | |
| F013 | Vassiliadou_2015 | 2015 | Greece | E612 | Squid | Loligo vulgaris | vertebrate | mollusca | 24.29 | PFOS | 8 | linear | Yes | Grilling | No liquid | NA | 180 | NA | No | No | NA | NA | 0.000 | NA | NA | C159 | Shared control | NA | 6.000000 | 1 | ng/g | 0.1000000 | <LOD | NA | sd | technical | 6.000000 | 1 | 1.1900000 | NA | 0.17 | technical | 1 | ng/g | 0.2 | 0.59 | Independent | Table 3 | No | Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper). For sample sizes, there are no sample sizes per treatment. They only provide the total number of specimens before dividing them up equally for each treatment. | NA | NA | 0.0000000 |
The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document
tree <- read.tree(here("data", "phylogenetic_tree.tre")) # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details)
tree <- compute.brlen(tree) # Generate branch lengths
cor_tree <- vcv(tree, corr = T) # Generate phylogenetic variance-covariance matrix
dat$Phylogeny <- as.factor(str_replace(dat$Species_Scientific, " ", "_")) # Add the `phylogeny` column to the data frame
colnames(cor_tree) %in% dat$Phylogeny # Check correspondence between tip names and data frame## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
# checking all species are in the data
# match(unique(dat$Phylogeny),colnames(cor_tree))
match(dat$Phylogeny, colnames(cor_tree))## [1] 12 14 28 28 28 28 28 28 13 32 32 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [26] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2
## [51] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7
## [76] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [101] 7 7 7 7 7 7 7 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
## [126] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 3 26 26 18 18 18 18 34 34 34
## [151] 34 34 34 22 22 31 31 30 30 30 30 30 23 23 23 23 1 1 1 1 1 1 1 1 1
## [176] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15 15
## [201] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 38 38 38
## [226] 38 38 38 38 38 38 38 38 38 38 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
## [251] 16 16 16 16 16 16 16 16 16 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27
## [276] 27 27 27 27 27 27 27 27 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [301] 8 8 8 8 8 8 8 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
## [326] 19 19 19 19 19 19 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [351] 9 9 9 9 9 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
## [376] 14 14 14 14 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
## [401] 17 17 17 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [426] 11 11 24 24 24 24 24 24 24 24 24 37 37 37 37 37 37 37 37 37 37 37 37 37 35
## [451] 35 35 35 35 35 35 5 5 5 20 20 20 29 29 29 29 21 21 10 10 10 4 27 27 27
## [476] 27 27 36 36 36 36 36 36 36 36 33 33 33 33 33 5 5 5 5 20 20 20 29 29 29
## [501] 29 29 4 4 4 27 27 27 27 33 33 33
# plotting tree
plot(tree)The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)
dat$Study_ID <- as.factor(dat$Study_ID)
dat$SDe <- as.numeric(dat$SDe)
# Calculate the squared coefficient of variation for control and experimental groups
aCV2 <- dat %>%
group_by(Study_ID) %>% # Group by study
summarise(CV2c = mean((SDc/Mc)^2, na.rm = T),
CV2e = mean((SDe/Me)^2, na.rm = T)) %>%
ungroup() %>% # ungroup
summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
aCV2e = mean(CV2e, na.rm = T))
lnRR <- # Calculate effect sizes
lnRR_func(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]])
var_lnRR <- ifelse(dat$Design == "Dependent", # Calculate sampling variance
var_lnRR_dep(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]],
rho = 0.5),
var_lnRR_ind(Mc = dat$Mc,
Nc = dat$Nc,
Me = dat$Me,
Ne = dat$Ne,
aCV2c = aCV2[[1]],
aCV2e = aCV2[[2]]))
dat <- dat %>%
mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # getting effective sample size
dat <- cbind(dat, lnRR, var_lnRR) # Merge effect sizes with the data frame
VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) +
theme_classic()# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
theme_classic()# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
scale_x_log10() + theme_classic()dat %>%
summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
`Studies` = n_distinct(Study_ID),
`Species` = n_distinct(Species_common),
`PFAS type` = n_distinct(PFAS_type),
`Cohorts` = n_distinct(Cohort_ID),
`Effect sizes` = n_distinct(Effect_ID),
`Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
`Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
`Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),
`Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
`Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
`Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),
`Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
`Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
`Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes
table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")| n (sample size) | |
|---|---|
| Studies | 10 |
| Species | 39 |
| PFAS type | 18 |
| Cohorts | 153 |
| Effect sizes | 512 |
| Effect sizes (Oil-based) | 303 |
| Studies (Oil-based) | 7 |
| Species (Oil-based) | 28 |
| Effect sizes (Water-based) | 140 |
| Studies (Water-based) | 8 |
| Species (Water-based) | 23 |
| Effect sizes (No liquid) | 69 |
| Studies (No liquid) | 2 |
| Species (No liquid) | 14 |
kable(summary(dat), "html") %>%
kable_styling("striped", position = "left") %>%
scroll_box(width = "100%", height = "500px")| Study_ID | Author_year | Publication_year | Country_firstAuthor | Effect_ID | Species_common | Species_Scientific | Invertebrate_vertebrate | Fish_mollusc | Moisture_loss_in_percent | PFAS_type | PFAS_carbon_chain | linear_total | Choice_of_9 | Cooking_method | Cooking_Category | Comments_cooking | Temperature_in_Celsius | Length_cooking_time_in_s | Water | Oil | Oil_type | Volume_liquid_ml | Volume_liquid_ml_0 | Ratio_liquid_fish | Weigh_g_sample | Cohort_ID | Cohort_comment | Cohort_comment_2 | Nc | Pooled_Nc | Unit_PFAS_conc | Mc | Mc_comment | Sc | sd | Sc_technical_biological | Ne | Pooled_Ne | Me | Me_comment | Se | Se_technical_biological | If_technical_how_many | Unit_LOD_LOQ | LOD | LOQ | Design | DataSource | Raw_data_provided | General_comments | checked | SDc | SDe | Ratio_liquid_fish_0 | Phylogeny | N_tilde | lnRR | var_lnRR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F010 :192 | Length:512 | Min. :2008 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 6.77 | Length:512 | Min. : 3.000 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 75.0 | Min. : 120.0 | Length:512 | Length:512 | Length:512 | Min. : 0.341 | Min. : 0.0 | Min. : 0.00266 | Min. : 10.0 | Length:512 | Length:512 | Length:512 | Min. : 1.000 | Min. :1.000 | Length:512 | Min. : 0.002 | Length:512 | Min. : 0.0010 | Length:512 | Length:512 | Min. : 1.000 | Min. :1.000 | Min. : 0.0020 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Length:512 | Min. : 0.0010 | Min. : 0.0010 | Min. : 0.00000 | Cyprinus_carpio : 33 | Min. : 0.500 | Min. :-6.0350 | Min. :0.01679 | |
| F003 :129 | Class :character | 1st Qu.:2014 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:14.45 | Class :character | 1st Qu.: 8.000 | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.:100.0 | 1st Qu.: 600.0 | Class :character | Class :character | Class :character | 1st Qu.: 11.000 | 1st Qu.: 5.0 | 1st Qu.: 0.10004 | 1st Qu.: 10.0 | Class :character | Class :character | Class :character | 1st Qu.: 5.000 | 1st Qu.:1.000 | Class :character | 1st Qu.: 0.160 | Class :character | 1st Qu.: 0.0010 | Class :character | Class :character | 1st Qu.: 5.000 | 1st Qu.:1.000 | 1st Qu.: 0.0940 | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | 1st Qu.: 0.0354 | 1st Qu.: 0.0585 | 1st Qu.: 0.05116 | Mullus_barbatus : 33 | 1st Qu.: 2.500 | 1st Qu.:-0.8778 | 1st Qu.:0.08393 | |
| F013 : 56 | Mode :character | Median :2019 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :18.35 | Mode :character | Median : 8.000 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median :160.0 | Median : 600.0 | Mode :character | Mode :character | Mode :character | Median : 300.000 | Median : 250.0 | Median : 2.50000 | Median : 70.0 | Mode :character | Mode :character | Mode :character | Median :10.000 | Median :1.000 | Mode :character | Median : 0.298 | Mode :character | Median : 0.0100 | Mode :character | Mode :character | Median :10.000 | Median :1.000 | Median : 0.2285 | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Median : 0.1580 | Median : 0.1460 | Median : 0.52000 | Salvelinus_namaycush : 33 | Median : 5.000 | Median :-0.1671 | Median :0.09270 | |
| F006 : 32 | NA | Mean :2017 | NA | NA | NA | NA | NA | NA | Mean :21.04 | NA | Mean : 8.994 | NA | NA | NA | NA | NA | Mean :161.3 | Mean : 733.3 | NA | NA | NA | Mean : 271.946 | Mean : 231.8 | Mean :13.58240 | Mean : 149.1 | NA | NA | NA | Mean : 8.486 | Mean :2.316 | NA | Mean : 3.494 | NA | Mean : 1.7676 | NA | NA | Mean : 8.486 | Mean :2.316 | Mean : 3.2322 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 4.4069 | Mean : 4.4491 | Mean :11.68141 | Sander_vitreus : 33 | Mean : 4.243 | Mean :-0.3633 | Mean :0.13527 | |
| F011 : 29 | NA | 3rd Qu.:2019 | NA | NA | NA | NA | NA | NA | 3rd Qu.:21.31 | NA | 3rd Qu.:11.000 | NA | NA | NA | NA | NA | 3rd Qu.:175.0 | 3rd Qu.: 900.0 | NA | NA | NA | 3rd Qu.: 300.000 | 3rd Qu.: 300.0 | 3rd Qu.:30.00000 | 3rd Qu.: 178.4 | NA | NA | NA | 3rd Qu.:10.000 | 3rd Qu.:5.000 | NA | 3rd Qu.: 1.083 | NA | 3rd Qu.: 0.1185 | NA | NA | 3rd Qu.:10.000 | 3rd Qu.:5.000 | 3rd Qu.: 1.0505 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 0.5600 | 3rd Qu.: 0.6518 | 3rd Qu.:30.00000 | Ctenopharyngodon_idell : 32 | 3rd Qu.: 5.000 | 3rd Qu.: 0.1848 | 3rd Qu.:0.16787 | |
| F005 : 26 | NA | Max. :2020 | NA | NA | NA | NA | NA | NA | Max. :79.11 | NA | Max. :14.000 | NA | NA | NA | NA | NA | Max. :300.0 | Max. :1500.0 | NA | NA | NA | Max. :2500.000 | Max. :2500.0 | Max. :45.33092 | Max. :1000.0 | NA | NA | NA | Max. :50.000 | Max. :6.000 | NA | Max. :86.689 | NA | Max. :133.7000 | NA | NA | Max. :50.000 | Max. :6.000 | Max. :134.4379 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Max. :133.7000 | Max. :130.5000 | Max. :45.33092 | Oncorhynchus_tshawytscha: 30 | Max. :25.000 | Max. : 3.4598 | Max. :0.83934 | |
| (Other): 48 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :284 | NA | NA | NA | NA | NA | NA | NA | NA’s :6 | NA’s :56 | NA | NA | NA | NA’s :114 | NA’s :45 | NA’s :88 | NA’s :106 | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :53 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :330 | NA’s :328 | NA’s :19 | (Other) :318 | NA | NA | NA |
Cohort_ID and Phylogeny explained virtually no variance in the model. Hence, they was removed from the model. All the other random effects explained significant variance and were kept in subsequent models
MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
random = list(~1|Study_ID, # Identity of the study
~1|Phylogeny, # Phylogenetic correlation
~1|Cohort_ID, # Identity of the cohort (shared controls)
~1|Species_common, # Non-phylogenetic correlation between species
~1|PFAS_type, # Type of PFAS
~1|Effect_ID), # Effect size identity
R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
test = "t",
data = dat,
sparse = TRUE)
summary(MA_all_rand_effects) # Cohort ID does not explain any variance ##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.2065 1244.4131 1258.4131 1288.0677 1258.6357
##
## Variance Components:
##
## estim sqrt nlvls fixed factor R
## sigma^2.1 0.5882 0.7670 10 no Study_ID no
## sigma^2.2 0.0000 0.0002 38 no Phylogeny yes
## sigma^2.3 0.0000 0.0000 153 no Cohort_ID no
## sigma^2.4 0.1777 0.4216 39 no Species_common no
## sigma^2.5 0.0983 0.3136 18 no PFAS_type no
## sigma^2.6 0.4786 0.6918 512 no Effect_ID no
##
## Test for Heterogeneity:
## Q(df = 511) = 7490.5888, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3366 0.2830 -1.1895 511 0.2348 -0.8926 0.2194
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MA_model <- rma.mv(lnRR, VCV_lnRR,
random = list(~1|Study_ID,
~1|Species_common, # Removed Cohort_ID and phylogeny
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = dat,
sparse = TRUE)
summary(MA_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -622.2065 1244.4131 1254.4131 1275.5949 1254.5319
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5882 0.7670 10 no Study_ID
## sigma^2.2 0.1777 0.4216 39 no Species_common
## sigma^2.3 0.0983 0.3136 18 no PFAS_type
## sigma^2.4 0.4786 0.6918 512 no Effect_ID
##
## Test for Heterogeneity:
## Q(df = 511) = 7490.5888, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.3366 0.2830 -1.1895 511 0.2348 -0.8926 0.2194
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
round(i2_ml(MA_model),2) # Percentage of heterogeneity explained by each random effect## I2_Total I2_Study_ID I2_Species_common I2_PFAS_type
## 92.92 40.70 12.30 6.80
## I2_Effect_ID
## 33.12
orchard_plot(MA_model, mod = "1", xlab = "lnRR", alpha=0.4, data = dat, group = "Study_ID", trunk.size=9, branch.size = 2) +
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models run_model<-function(data,formula){
data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix
VCV<-make_VCV_matrix(data
, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
rma.mv(lnRR, VCV, # run the model, as described earlier
mods=formula,
random = list(~1|Study_ID,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = data,
sparse=TRUE) # Make the model run faster
}plot_continuous<-function(data, model, moderator, xlab){
pred<-predict.rma(model)
data %>% mutate(fit=pred$pred,
ci.lb=pred$ci.lb,
ci.ub=pred$ci.ub,
pr.lb=pred$cr.lb,
pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
geom_line(aes(y = fit), size = 1.5)+ # Regression line
labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
theme_bw() +
scale_size_continuous(range=c(1,9))+ # Point scaling
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))
}All continuous variables were z-transformed.
Note that all analyses involving the liquid/animal tissue ratio were ran by considering ratio values for the no-liquid cooking category as either “NA” or “0”. The results presented in the manuscript consider the ratio to be 0.
# Length_cooking_time_in_s
time_model <- run_model(dat, ~scale(Length_cooking_time_in_s)) # z-transformed
summary(time_model)##
## Multivariate Meta-Analysis Model (k = 456; method: REML)
##
## logLik Deviance AIC BIC AICc
## -515.8654 1031.7308 1043.7308 1068.4393 1043.9187
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5315 0.7290 9 no Study_ID
## sigma^2.2 0.1701 0.4124 30 no Species_common
## sigma^2.3 0.0988 0.3143 17 no PFAS_type
## sigma^2.4 0.4094 0.6398 456 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 454) = 6619.4345, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 27.5934, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5570 0.2874 -1.9377 454 0.0533
## scale(Length_cooking_time_in_s) -0.2565 0.0488 -5.2529 454 <.0001
## ci.lb ci.ub
## intrcpt -1.1218 0.0079 .
## scale(Length_cooking_time_in_s) -0.3524 -0.1605 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(time_model) # Estimate R squared## R2_marginal R2_conditional
## 0.05157579 0.67902811
# Plot
dat.time <- filter(dat, Length_cooking_time_in_s != "NA") # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")0volume0_model <- run_model(dat, ~scale(log(Ratio_liquid_fish_0 + 1))) # logged and z-transformed after adding 1
summary(volume0_model)##
## Multivariate Meta-Analysis Model (k = 493; method: REML)
##
## logLik Deviance AIC BIC AICc
## -593.3771 1186.7541 1198.7541 1223.9328 1198.9277
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6120 0.7823 8 no Study_ID
## sigma^2.2 0.1610 0.4013 35 no Species_common
## sigma^2.3 0.1235 0.3514 18 no PFAS_type
## sigma^2.4 0.4681 0.6842 493 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 491) = 6429.2787, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 491) = 4.5602, p-val = 0.0332
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.4507 0.3135 -1.4374 491 0.1512
## scale(log(Ratio_liquid_fish_0 + 1)) -0.1161 0.0543 -2.1355 491 0.0332
## ci.lb ci.ub
## intrcpt -1.0667 0.1654
## scale(log(Ratio_liquid_fish_0 + 1)) -0.2228 -0.0093 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume0_model)## R2_marginal R2_conditional
## 0.009773911 0.660335896
# Plot
dat.volume0 <- filter(dat, Ratio_liquid_fish_0 != "NA")
plot_continuous(dat.volume0, volume0_model, log(dat.volume0$Ratio_liquid_fish_0 +
1), "ln (Liquid volume to tissue sample ratio + 1)")NA# Ratio_liquid_fish
dat <- dat %>%
mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category == "No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is 'No liquid', otherwise keep the same value of Ratio_liquid_fish
volume_model <- run_model(dat, ~scale(log(Ratio_liquid_fish))) # logged and z-transformed
summary(volume_model)##
## Multivariate Meta-Analysis Model (k = 424; method: REML)
##
## logLik Deviance AIC BIC AICc
## -527.2307 1054.4614 1066.4614 1090.7314 1066.6638
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5651 0.7517 8 no Study_ID
## sigma^2.2 0.0000 0.0001 35 no Species_common
## sigma^2.3 0.1173 0.3424 18 no PFAS_type
## sigma^2.4 0.5461 0.7390 424 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 6027.2722, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 422) = 6.7104, p-val = 0.0099
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.4646 0.2895 -1.6050 422 0.1092 -1.0335
## scale(log(Ratio_liquid_fish)) -0.3004 0.1160 -2.5904 422 0.0099 -0.5284
## ci.ub
## intrcpt 0.1044
## scale(log(Ratio_liquid_fish)) -0.0725 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume_model)## R2_marginal R2_conditional
## 0.06844458 0.58587340
# Plot
dat.volume <- filter(dat, Ratio_liquid_fish != "NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Liquid volume to tissue sample ratio)")# Temperature_in_Celsius
temp_model <- run_model(dat, ~scale(Temperature_in_Celsius)) # z-transformed
summary(temp_model)##
## Multivariate Meta-Analysis Model (k = 506; method: REML)
##
## logLik Deviance AIC BIC AICc
## -613.6186 1227.2372 1239.2372 1264.5727 1239.4063
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5827 0.7634 10 no Study_ID
## sigma^2.2 0.1749 0.4182 39 no Species_common
## sigma^2.3 0.0952 0.3086 18 no PFAS_type
## sigma^2.4 0.4797 0.6926 506 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 504) = 7359.9662, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0239, p-val = 0.8773
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.3216 0.2835 -1.1345 504 0.2571 -0.8785
## scale(Temperature_in_Celsius) 0.0110 0.0712 0.1545 504 0.8773 -0.1288
## ci.ub
## intrcpt 0.2353
## scale(Temperature_in_Celsius) 0.1508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(temp_model)## R2_marginal R2_conditional
## 9.072275e-05 6.400161e-01
# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")# PFAS_carbon_chain
PFAS_model <- run_model(dat, ~PFAS_carbon_chain)
summary(PFAS_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -620.7200 1241.4400 1253.4400 1278.8464 1253.6070
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5872 0.7663 10 no Study_ID
## sigma^2.2 0.1795 0.4237 39 no Species_common
## sigma^2.3 0.1028 0.3207 18 no PFAS_type
## sigma^2.4 0.4788 0.6920 512 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 7490.4771, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1785, p-val = 0.6729
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.4548 0.3975 -1.1441 510 0.2531 -1.2359 0.3262
## PFAS_carbon_chain 0.0130 0.0308 0.4225 510 0.6729 -0.0474 0.0734
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(PFAS_model)## R2_marginal R2_conditional
## 0.0006792794 0.6451388582
plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")# Cooking_Category
category_model<-run_model(dat, ~Cooking_Category-1)
summary(category_model)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -619.1879 1238.3758 1252.3758 1282.0029 1252.5994
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5917 0.7692 10 no Study_ID
## sigma^2.2 0.1821 0.4268 39 no Species_common
## sigma^2.3 0.0994 0.3153 18 no PFAS_type
## sigma^2.4 0.4779 0.6913 512 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 509) = 7488.0974, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.2492, p-val = 0.2913
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Cooking_CategoryNo liquid -0.2128 0.3061 -0.6951 509 0.4873 -0.8142
## Cooking_Categoryoil-based -0.3940 0.2908 -1.3548 509 0.1761 -0.9652
## Cooking_Categorywater-based -0.3085 0.2888 -1.0681 509 0.2860 -0.8759
## ci.ub
## Cooking_CategoryNo liquid 0.3886
## Cooking_Categoryoil-based 0.1773
## Cooking_Categorywater-based 0.2589
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(category_model)## R2_marginal R2_conditional
## 0.003059016 0.647383155
# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4, data = dat, group = "Study_ID", trunk.size=9, branch.size = 2) +
scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))This analysis is a posteriori and will only be presented in supplement.
# Moisture_loss_in_percent
moisture_model <- run_model(dat, ~scale(Moisture_loss_in_percent))
summary(moisture_model)##
## Multivariate Meta-Analysis Model (k = 228; method: REML)
##
## logLik Deviance AIC BIC AICc
## -212.0065 424.0130 436.0130 456.5362 436.3965
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0532 0.2307 6 no Study_ID
## sigma^2.2 0.2422 0.4921 18 no Species_common
## sigma^2.3 0.0094 0.0968 17 no PFAS_type
## sigma^2.4 0.2127 0.4612 228 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 226) = 1173.1726, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.0020, p-val = 0.9648
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.3614 0.1811 1.9954 226 0.0472 0.0045
## scale(Moisture_loss_in_percent) 0.0035 0.0783 0.0442 226 0.9648 -0.1508
## ci.ub
## intrcpt 0.7183 *
## scale(Moisture_loss_in_percent) 0.1577
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(moisture_model)## R2_marginal R2_conditional
## 2.316463e-05 5.889920e-01
# Plot
dat.moisture <- filter(dat, Moisture_loss_in_percent != "NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent,
"Percentage of moisture loss")save(category_model, PFAS_model, temp_model, time_model, volume_model, volume0_model,
moisture_model, file = here("Rdata", "single_mod_models.RData")) # Save models0 for the dry cooking categoryfull_model0 <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
1)), random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -455.3622 910.7244 932.7244 977.2715 933.3652
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3890 0.6237 7 no Study_ID
## sigma^2.2 0.1514 0.3891 26 no Species_common
## sigma^2.3 0.1362 0.3690 17 no PFAS_type
## sigma^2.4 0.3598 0.5998 431 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 424) = 5424.4518, p-val < .0001
##
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 424) = 12.3675, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -2.4312 0.4296 -5.6587 424 <.0001
## Cooking_Categoryoil-based 1.6525 0.3294 5.0160 424 <.0001
## Cooking_Categorywater-based 1.9173 0.3785 5.0650 424 <.0001
## scale(Temperature_in_Celsius) -0.0019 0.0982 -0.0189 424 0.9850
## scale(Length_cooking_time_in_s) -0.3739 0.0500 -7.4756 424 <.0001
## scale(PFAS_carbon_chain) 0.0639 0.0811 0.7873 424 0.4316
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8572 0.1458 -5.8808 424 <.0001
## ci.lb ci.ub
## intrcpt -3.2757 -1.5867 ***
## Cooking_Categoryoil-based 1.0049 2.3000 ***
## Cooking_Categorywater-based 1.1732 2.6613 ***
## scale(Temperature_in_Celsius) -0.1949 0.1912
## scale(Length_cooking_time_in_s) -0.4722 -0.2756 ***
## scale(PFAS_carbon_chain) -0.0956 0.2233
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1437 -0.5707 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model0)## R2_marginal R2_conditional
## 0.4884675 0.8224021
save(full_model0, file = here("Rdata", "full_model.RData"))NA for the dry cooking category# Testing cooking categories
full_model <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)),
random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)
summary(full_model)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -429.9562 859.9125 879.9125 919.2614 880.5119
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3874 0.6224 7 no Study_ID
## sigma^2.2 0.1364 0.3694 26 no Species_common
## sigma^2.3 0.1234 0.3512 17 no PFAS_type
## sigma^2.4 0.4128 0.6425 384 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 378) = 5318.7107, p-val < .0001
##
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.7130, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.8603 0.3039 -2.8311 378 0.0049
## Cooking_Categoryoil-based 0.1302 0.1630 0.7992 378 0.4247
## scale(Temperature_in_Celsius) -0.3422 0.1260 -2.7154 378 0.0069
## scale(Length_cooking_time_in_s) -0.3316 0.0562 -5.8997 378 <.0001
## scale(PFAS_carbon_chain) 0.0614 0.0801 0.7666 378 0.4438
## scale(log(Ratio_liquid_fish)) -0.8152 0.1683 -4.8449 378 <.0001
## ci.lb ci.ub
## intrcpt -1.4578 -0.2628 **
## Cooking_Categoryoil-based -0.1902 0.4507
## scale(Temperature_in_Celsius) -0.5899 -0.0944 **
## scale(Length_cooking_time_in_s) -0.4422 -0.2211 ***
## scale(PFAS_carbon_chain) -0.0961 0.2189
## scale(log(Ratio_liquid_fish)) -1.1460 -0.4843 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model)## R2_marginal R2_conditional
## 0.3637260 0.7522155
save(full_model, file = here("Rdata", "full_model.RData"))## Check for collinerarity - seems fine
vif(full_model)##
## Cooking_Categoryoil-based scale(Temperature_in_Celsius)
## 2.1467 3.1661
## scale(Length_cooking_time_in_s) scale(PFAS_carbon_chain)
## 1.0583 1.0001
## scale(log(Ratio_liquid_fish))
## 1.7682
vif(full_model0)##
## Cooking_Categoryoil-based Cooking_Categorywater-based
## 13.1568 13.8293
## scale(Temperature_in_Celsius) scale(Length_cooking_time_in_s)
## 2.1458 1.0818
## scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish_0 + 1))
## 1.0001 8.5882
dat %>%
select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
ggpairs() # Estimate correlations between the variablesInspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.
Note that these analyses were not performed separately using full models with Ratio_liquid_fish taken as NA or 0. Indeed, a full model containing the dry cooking category and the liquid ratio would extrapolate predictions for the dry cooking category at the mean liquid ratio; which is incorrect. Therefore, all full models were ran with the data containing NA for the Ratio_liquid_fish of the dry cooking method; and separate models were ran with a data subset only containing the dry cooking method.
# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish <- log(dat$Ratio_liquid_fish)
full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), but without the 'No
# liquid' data This model will be used for conditional analyses on the volume
# of liquid, where the data without liquid is irrelevant
dat_oil_water <- filter(dat, Cooking_Category != "No liquid")
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# Full model in original units (no z-transformation), with Ratio_liquid_fish_0
dat$log_Ratio_liquid_fish0 <- log(dat$Ratio_liquid_fish_0 + 1)
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# Data subset only containing the data with the dry cooking method. Here, only
# the cooking time was added because the liquid ratio, cooking temperature, and
# PFAS carbon chain length do not have sufficient variability.
dat_dry <- filter(dat, Cooking_Category == "No liquid")
full_model_org_units_dry <- run_model(dat_dry, ~Length_cooking_time_in_s)
save(full_model_org_units, full_model_org_units_dry, full_model_org_units0, full_model_org_units_oil_water,
file = here("Rdata", "full_models_org_units.RData"))0 for the dry cooking categoryres0<-mod_results(model = full_model_org_units0, data=dat, group="Study_ID",mod="1")
res0$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.9901905 -1.556167 -0.4242139 -3.069671 1.08929
orchard_plot(res0, mod="1", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))NA for the dry cooking categoryres<-mod_results(model = full_model_org_units, data=dat, group="Study_ID", mod="1")
res$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.7973281 -1.353966 -0.2406905 -2.896781 1.302125
orchard_plot(res, mod="1", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cat<-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",by = "Cooking_Category")
res_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt oil-based -0.7562916 -1.320986 -0.1915969 -2.857895 1.345312
## 2 Intrcpt water-based -0.8865230 -1.486962 -0.2860837 -2.998012 1.224966
orchard_plot(res_cat, mod="1", condition.lab="Cooking Category", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_dry<-mod_results(full_model_org_units_dry, data = dat_dry, group="Study_ID", mod = "1")
res_dry$mod_table## name estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -0.7902119 -1.193101 -0.3873229 -1.542195 -0.03822837
orchard_plot(res_dry, mod="1", group= "Study_ID", xlab="lnRR", data=dat_dry, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))Here, we generate estimates at cooking times of 2, 10, and 25 min.
0 for the dry cooking categoryres_cooking_time0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1983672 -0.7807464 0.3840120 -2.282371 1.8856370
## 2 Intrcpt 600 -0.8124488 -1.3754870 -0.2494106 -2.891131 1.2662335
## 3 Intrcpt 1500 -1.9638518 -2.6088373 -1.3188663 -4.066211 0.1385075
orchard_plot(res_cooking_time0, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))NA for the dry cooking categoryres_cooking_time <-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 -0.1164802 -0.7011029 0.4681425 -2.223526 1.9905653
## 2 Intrcpt 600 -0.6611585 -1.2161311 -0.1061859 -2.760170 1.4378535
## 3 Intrcpt 1500 -1.6824303 -2.3322614 -1.0325992 -3.808492 0.4436311
orchard_plot(res_cooking_time, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cooking_time_cat <-mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 120 -0.07544371 -0.6710478 0.52016034 -2.185562 2.0346750
## 2 Water-based 120 -0.20567515 -0.8252984 0.41394812 -2.322699 1.9113487
## 3 Oil-based 600 -0.62012201 -1.1838724 -0.05637162 -2.721472 1.4812279
## 4 Water-based 600 -0.75035346 -1.3478232 -0.15288372 -2.861000 1.3602926
## 5 Oil-based 1500 -1.64139383 -2.2942428 -0.98854484 -3.768380 0.4855920
## 6 Water-based 1500 -1.77162528 -2.4673659 -1.07588460 -3.912165 0.3689148
orchard_plot(res_cooking_time_cat, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_cooking_time_dry <-mod_results(full_model_org_units_dry, data = dat_dry, group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
res_cooking_time_dry$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 120 0.3152385 -0.2249497 0.8554266 -0.5184059 1.1488828
## 2 Intrcpt 600 -0.3706515 -0.7962774 0.0549744 -1.1350579 0.3937549
## 3 Intrcpt 1500 -1.6566952 -2.1473771 -1.1660133 -2.4591467 -0.8542437
orchard_plot(res_cooking_time_dry, mod="1", condition.lab="Cooking time (sec)", group= "Study_ID", xlab="lnRR", data=dat_dry, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left", k.pos="left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))NA for the dry cooking categoryHere, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis when considering Ratio_liquid_fish as NA.
res_volume<-mod_results(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))), by = "log_Ratio_liquid_fish")
res_volume$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt -2.302585 -0.09956816 -0.7147303 0.5155939 -2.215291 2.0161542
## 2 Intrcpt 2.302585 -1.27359673 -1.8695250 -0.6776685 -3.383807 0.8366135
## 3 Intrcpt 3.806662 -1.65704181 -2.3247881 -0.9892956 -3.788647 0.4745637
orchard_plot(res_volume, mod="1", condition.lab="ln(Liquid volume to tissue sample ratio) (mL/g)", group= "Study_ID", xlab="lnRR", data=dat_oil_water, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))Here, we generate marginalised estimates at volumes of liquid of 0mL/g of tissue (dry cooking), ~10 ml/g of tissue, or 45 mL/g of tissue.
res_volume0 <- mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish0 = c(0, log(10 + 1), log(45 + 1))), by = "log_Ratio_liquid_fish0")
res_volume0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 0.000000 -0.09267475 -0.6981902 0.5128407 -2.183263 1.99791300
## 2 Intrcpt 2.397895 -1.40207347 -2.0015084 -0.8026386 -3.490908 0.68676123
## 3 Intrcpt 3.828641 -2.18334909 -2.9164402 -1.4502579 -4.314389 -0.05230924
orchard_plot(res_volume0, mod="1", condition.lab="ln(Liquid volume to tissue sample ratio) (mL/g)", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))0 for the dry cooking categoryres_PFAS0<-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS0$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -1.1531770 -1.857765 -0.4485891 -3.274580 0.9682264
## 2 Intrcpt 6 -1.0709524 -1.676100 -0.4658052 -3.161434 1.0195287
## 3 Intrcpt 12 -0.9065034 -1.505319 -0.3076879 -2.995160 1.1821537
orchard_plot(res_PFAS0, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))NA for the dry cooking categoryHere, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains
res_PFAS<-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID",at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Intrcpt 3 -0.9570424 -1.650058 -0.2640265 -3.096698 1.182614
## 2 Intrcpt 6 -0.8780085 -1.472976 -0.2830409 -2.987948 1.231931
## 3 Intrcpt 12 -0.7199407 -1.309841 -0.1300409 -2.828456 1.388575
orchard_plot(res_PFAS, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))res_PFAS_cat<-mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID", at = list(PFAS_carbon_chain= c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table## name condition estimate lowerCL upperCL lowerPR upperPR
## 1 Oil-based 3 -0.9160059 -1.615262 -0.21674972 -3.057691 1.225679
## 2 Water-based 3 -1.0462374 -1.775419 -0.31705550 -3.197879 1.105405
## 3 Oil-based 6 -0.8369720 -1.439339 -0.23460491 -2.949010 1.275066
## 4 Water-based 6 -0.9672035 -1.603638 -0.33076892 -3.089209 1.154802
## 5 Oil-based 12 -0.6789042 -1.276553 -0.08125509 -2.789601 1.431793
## 6 Water-based 12 -0.8091357 -1.440245 -0.17802640 -2.929550 1.311278
orchard_plot(res_PFAS_cat, mod="1", condition.lab="PFAS carbon chain", group= "Study_ID", xlab="lnRR", data=dat, alpha=0.4, trunk.size = 9, branch.size = 2, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13))Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.
oil_dat <- filter(dat, Cooking_Category == "oil-based")0 for the dry cooking categoryfull_model_oil0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_oil0)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -174.9150 349.8301 367.8301 399.8067 368.5559
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1110 0.3331 6 no Study_ID
## sigma^2.2 0.0225 0.1501 19 no Species_common
## sigma^2.3 0.0510 0.2257 16 no PFAS_type
## sigma^2.4 0.1287 0.3587 263 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1001.2025, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 18.4866, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5811 0.1787 -3.2522 258 0.0013
## scale(Temperature_in_Celsius) -0.0158 0.0786 -0.2014 258 0.8406
## scale(Length_cooking_time_in_s) -0.3791 0.0465 -8.1479 258 <.0001
## scale(PFAS_carbon_chain) 0.1287 0.0621 2.0737 258 0.0391
## scale(log(Ratio_liquid_fish_0 + 1)) -0.3162 0.1809 -1.7485 258 0.0816
## ci.lb ci.ub
## intrcpt -0.9329 -0.2292 **
## scale(Temperature_in_Celsius) -0.1706 0.1390
## scale(Length_cooking_time_in_s) -0.4708 -0.2875 ***
## scale(PFAS_carbon_chain) 0.0065 0.2509 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6724 0.0399 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil0)## R2_marginal R2_conditional
## 0.5608854 0.8195486
save(full_model_oil0, file = here("Rdata", "full_model_oil0.RData"))NA for the dry cooking categoryfull_model_oil <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))
summary(full_model_oil)##
## Multivariate Meta-Analysis Model (k = 263; method: REML)
##
## logLik Deviance AIC BIC AICc
## -176.0353 352.0705 370.0705 402.0472 370.7964
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1147 0.3387 6 no Study_ID
## sigma^2.2 0.0252 0.1586 19 no Species_common
## sigma^2.3 0.0485 0.2203 16 no PFAS_type
## sigma^2.4 0.1293 0.3596 263 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 258) = 1004.5329, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 17.9276, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.5750 0.1811 -3.1747 258 0.0017
## scale(Temperature_in_Celsius) -0.0869 0.1173 -0.7409 258 0.4594
## scale(Length_cooking_time_in_s) -0.3782 0.0468 -8.0905 258 <.0001
## scale(PFAS_carbon_chain) 0.1283 0.0613 2.0921 258 0.0374
## scale(log(Ratio_liquid_fish)) -0.2049 0.2022 -1.0137 258 0.3117
## ci.lb ci.ub
## intrcpt -0.9316 -0.2183 **
## scale(Temperature_in_Celsius) -0.3179 0.1441
## scale(Length_cooking_time_in_s) -0.4703 -0.2862 ***
## scale(PFAS_carbon_chain) 0.0075 0.2491 *
## scale(log(Ratio_liquid_fish)) -0.6030 0.1932
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil)## R2_marginal R2_conditional
## 0.4422561 0.7730136
save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))water_dat <- filter(dat, Cooking_Category == "water-based")0 for the dry cooking categoryfull_model_water0 <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_water0)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5919 357.1837 373.1837 395.2811 374.5171
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5836 0.7640 6 no Study_ID
## sigma^2.2 0.0000 0.0001 19 no Species_common
## sigma^2.3 0.5469 0.7396 15 no PFAS_type
## sigma^2.4 0.9339 0.9664 121 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4081.7044, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.1187, p-val = 0.0081
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3207 0.4140 -3.1905 117 0.0018
## scale(Length_cooking_time_in_s) -0.3681 0.1578 -2.3331 117 0.0214
## scale(PFAS_carbon_chain) -0.0497 0.1817 -0.2737 117 0.7848
## scale(log(Ratio_liquid_fish_0 + 1)) -0.6340 0.2484 -2.5521 117 0.0120
## ci.lb ci.ub
## intrcpt -2.1406 -0.5009 **
## scale(Length_cooking_time_in_s) -0.6806 -0.0556 *
## scale(PFAS_carbon_chain) -0.4096 0.3102
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1259 -0.1420 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water0)## R2_marginal R2_conditional
## 0.2073497 0.6414238
NA for the dry cooking categoryfull_model_water <- run_model(water_dat, ~scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(full_model_water)##
## Multivariate Meta-Analysis Model (k = 121; method: REML)
##
## logLik Deviance AIC BIC AICc
## -178.5993 357.1986 373.1986 395.2960 374.5319
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5919 0.7693 6 no Study_ID
## sigma^2.2 0.0000 0.0001 19 no Species_common
## sigma^2.3 0.5456 0.7386 15 no PFAS_type
## sigma^2.4 0.9342 0.9666 121 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 117) = 4082.8198, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.0885, p-val = 0.0084
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -1.3267 0.4164 -3.1862 117 0.0018
## scale(Length_cooking_time_in_s) -0.3729 0.1580 -2.3607 117 0.0199
## scale(PFAS_carbon_chain) -0.0493 0.1816 -0.2716 117 0.7864
## scale(log(Ratio_liquid_fish)) -0.6466 0.2552 -2.5337 117 0.0126
## ci.lb ci.ub
## intrcpt -2.1514 -0.5021 **
## scale(Length_cooking_time_in_s) -0.6858 -0.0601 *
## scale(PFAS_carbon_chain) -0.4090 0.3103
## scale(log(Ratio_liquid_fish)) -1.1520 -0.1412 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water)## R2_marginal R2_conditional
## 0.2112076 0.6442884
In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories
water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking
full_model_water_steamed <- run_model(water_dat, ~ -1 + # without intercept
steamed +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown.
summary(full_model_water_steamed)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.4827 420.9654 436.9654 460.2666 438.0992
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6898 0.8305 8 no Study_ID
## sigma^2.2 0.0495 0.2225 23 no Species_common
## sigma^2.3 0.2682 0.5179 15 no PFAS_type
## sigma^2.4 0.9794 0.9896 140 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4581.2864, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 136) = 1.8935, p-val = 0.1151
##
## Model Results:
##
## estimate se tval df pval
## steamedother -0.7274 0.3850 -1.8894 136 0.0610
## steamedsteamed -0.5600 0.4478 -1.2504 136 0.2133
## scale(Length_cooking_time_in_s) -0.3054 0.1591 -1.9201 136 0.0569
## scale(PFAS_carbon_chain) -0.0540 0.1413 -0.3818 136 0.7032
## ci.lb ci.ub
## steamedother -1.4886 0.0339 .
## steamedsteamed -1.4456 0.3257
## scale(Length_cooking_time_in_s) -0.6200 0.0091 .
## scale(PFAS_carbon_chain) -0.3335 0.2255
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model(water_dat,
~ steamed + # with intercept
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain))
summary(full_model_water_steamed_cont)##
## Multivariate Meta-Analysis Model (k = 140; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.4827 420.9654 436.9654 460.2666 438.0992
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.6898 0.8305 8 no Study_ID
## sigma^2.2 0.0495 0.2225 23 no Species_common
## sigma^2.3 0.2682 0.5179 15 no PFAS_type
## sigma^2.4 0.9794 0.9896 140 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 136) = 4581.2864, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 136) = 1.3449, p-val = 0.2625
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.7274 0.3850 -1.8894 136 0.0610
## steamedsteamed 0.1674 0.4028 0.4156 136 0.6783
## scale(Length_cooking_time_in_s) -0.3054 0.1591 -1.9201 136 0.0569
## scale(PFAS_carbon_chain) -0.0540 0.1413 -0.3818 136 0.7032
## ci.lb ci.ub
## intrcpt -1.4886 0.0339 .
## steamedsteamed -0.6291 0.9639
## scale(Length_cooking_time_in_s) -0.6200 0.0091 .
## scale(PFAS_carbon_chain) -0.3335 0.2255
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))dry_dat <- filter(dat, Cooking_Category == "No liquid")full_model_dry <- run_model(dry_dat, ~scale(Length_cooking_time_in_s))
summary(full_model_dry)##
## Multivariate Meta-Analysis Model (k = 47; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.4570 22.9140 32.9140 41.9474 34.4525
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0000 0.0000 1 yes Study_ID
## sigma^2.2 0.0026 0.0513 8 no Species_common
## sigma^2.3 0.0731 0.2703 2 no PFAS_type
## sigma^2.4 0.0237 0.1540 47 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 45) = 99.2634, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 38.5197, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.7902 0.2000 -3.9504 45 0.0003 -1.1931
## scale(Length_cooking_time_in_s) -0.3518 0.0567 -6.2064 45 <.0001 -0.4660
## ci.ub
## intrcpt -0.3873 ***
## scale(Length_cooking_time_in_s) -0.2376 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_dry)## R2_marginal R2_conditional
## 0.5546179 0.8937802
save(full_model_dry, file = here("Rdata", "full_model_dry.RData"))0 for the dry cooking categoryfunnel(full_model0, yaxis = "seinv")funnel(full_model0)NA for the dry cooking categoryfunnel(full_model, yaxis = "seinv")funnel(full_model)0 for the dry cooking categoryegger_all0 <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(egger_all0)##
## Multivariate Meta-Analysis Model (k = 431; method: REML)
##
## logLik Deviance AIC BIC AICc
## -450.0464 900.0928 926.0928 978.6779 926.9850
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1184 0.3441 7 no Study_ID
## sigma^2.2 0.1679 0.4098 26 no Species_common
## sigma^2.3 0.1407 0.3751 17 no PFAS_type
## sigma^2.4 0.3592 0.5993 431 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 422) = 5069.2680, p-val < .0001
##
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 422) = 11.0529, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_CategoryNo liquid -2.3292 0.4540 -5.1309 422 <.0001
## Cooking_Categoryoil-based -0.7171 0.3927 -1.8261 422 0.0685
## Cooking_Categorywater-based -0.4585 0.4049 -1.1324 422 0.2581
## I(sqrt(1/N_tilde)) 0.0360 0.6060 0.0595 422 0.9526
## scale(Publication_year) 0.3541 0.1260 2.8111 422 0.0052
## scale(Temperature_in_Celsius) 0.0081 0.0966 0.0842 422 0.9329
## scale(Length_cooking_time_in_s) -0.3681 0.0490 -7.5162 422 <.0001
## scale(PFAS_carbon_chain) 0.0746 0.0820 0.9099 422 0.3634
## scale(log(Ratio_liquid_fish_0 + 1)) -0.8370 0.1353 -6.1864 422 <.0001
## ci.lb ci.ub
## Cooking_CategoryNo liquid -3.2215 -1.4369 ***
## Cooking_Categoryoil-based -1.4889 0.0548 .
## Cooking_Categorywater-based -1.2545 0.3374
## I(sqrt(1/N_tilde)) -1.1551 1.2272
## scale(Publication_year) 0.1065 0.6017 **
## scale(Temperature_in_Celsius) -0.1817 0.1980
## scale(Length_cooking_time_in_s) -0.4644 -0.2719 ***
## scale(PFAS_carbon_chain) -0.0866 0.2359
## scale(log(Ratio_liquid_fish_0 + 1)) -1.1029 -0.5710 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
NA for the dry cooking categoryegger_all <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
summary(egger_all)##
## Multivariate Meta-Analysis Model (k = 384; method: REML)
##
## logLik Deviance AIC BIC AICc
## -423.3298 846.6596 870.6596 917.8147 871.5191
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0213 0.1459 7 no Study_ID
## sigma^2.2 0.1700 0.4124 26 no Species_common
## sigma^2.3 0.1249 0.3534 17 no PFAS_type
## sigma^2.4 0.4096 0.6400 384 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 376) = 4938.5713, p-val < .0001
##
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 14.1757, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Cooking_Categoryoil-based -0.7006 0.3739 -1.8738 376 0.0617
## Cooking_Categorywater-based -0.8498 0.3738 -2.2736 376 0.0236
## I(sqrt(1/N_tilde)) 0.1514 0.6070 0.2493 376 0.8032
## scale(Publication_year) 0.4248 0.0896 4.7434 376 <.0001
## scale(Temperature_in_Celsius) -0.3518 0.1228 -2.8652 376 0.0044
## scale(Length_cooking_time_in_s) -0.3322 0.0534 -6.2229 376 <.0001
## scale(PFAS_carbon_chain) 0.0808 0.0802 1.0075 376 0.3143
## scale(log(Ratio_liquid_fish)) -0.9162 0.1490 -6.1491 376 <.0001
## ci.lb ci.ub
## Cooking_Categoryoil-based -1.4359 0.0346 .
## Cooking_Categorywater-based -1.5847 -0.1149 *
## I(sqrt(1/N_tilde)) -1.0422 1.3449
## scale(Publication_year) 0.2487 0.6009 ***
## scale(Temperature_in_Celsius) -0.5932 -0.1104 **
## scale(Length_cooking_time_in_s) -0.4371 -0.2272 ***
## scale(PFAS_carbon_chain) -0.0769 0.2385
## scale(log(Ratio_liquid_fish)) -1.2092 -0.6232 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
egger_n <- run_model(dat, ~I(sqrt(1/N_tilde)))
summary(egger_n)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -620.6237 1241.2474 1253.2474 1278.6539 1253.4144
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5898 0.7680 10 no Study_ID
## sigma^2.2 0.1814 0.4259 39 no Species_common
## sigma^2.3 0.0982 0.3134 18 no PFAS_type
## sigma^2.4 0.4790 0.6921 512 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 7467.4550, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.0075, p-val = 0.9310
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3621 0.4112 -0.8805 510 0.3790 -1.1700 0.4458
## I(sqrt(1/N_tilde)) 0.0521 0.6014 0.0866 510 0.9310 -1.1295 1.2336
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(egger_all, egger_all0, egger_n, file = here("Rdata", "egger_regressions.RData"))pub_year <- run_model(dat, ~Publication_year)
summary(pub_year)##
## Multivariate Meta-Analysis Model (k = 512; method: REML)
##
## logLik Deviance AIC BIC AICc
## -619.0233 1238.0465 1250.0465 1275.4530 1250.2135
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.5626 0.7500 10 no Study_ID
## sigma^2.2 0.1763 0.4199 39 no Species_common
## sigma^2.3 0.0987 0.3142 18 no PFAS_type
## sigma^2.4 0.4788 0.6920 512 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 7432.5901, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.3008, p-val = 0.2546
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -165.5866 144.8912 -1.1428 510 0.2536 -450.2436
## Publication_year 0.0819 0.0718 1.1405 510 0.2546 -0.0592
## ci.ub
## intrcpt 119.0704
## Publication_year 0.2231
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat, pub_year, dat$Publication_year, "Publication year") ##
Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one specific study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.
dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies
VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
random = list(~1|Study_ID,
~1|Species_common,
~1|PFAS_type,
~1|Effect_ID),
test = "t",
data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}
# The output is a list so we need to summarise the coefficients of all the models performed
results.Leave1studyout<-as.data.frame(cbind(
sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
sapply(Leave1studyout, function(x) summary(x)$zval), # extract the z value from all models
sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models
colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models| Estimate | SE | zval | pval | ci.lb | ci.ub |
|---|---|---|---|---|---|
| -0.3458077 | 0.3029849 | -1.1413364 | 0.2542666 | -0.9410625 | 0.2494472 |
| -0.4196589 | 0.3047387 | -1.3771106 | 0.1690918 | -1.0183792 | 0.1790615 |
| -0.4439445 | 0.3304790 | -1.3433365 | 0.1799605 | -1.0937301 | 0.2058412 |
| -0.0668046 | 0.2192848 | -0.3046476 | 0.7607652 | -0.4976701 | 0.3640610 |
| -0.3500494 | 0.3088332 | -1.1334577 | 0.2575891 | -0.9568847 | 0.2567859 |
| -0.2580536 | 0.2968634 | -0.8692672 | 0.3851292 | -0.8413447 | 0.3252375 |
| -0.3540688 | 0.3068927 | -1.1537216 | 0.2491677 | -0.9570329 | 0.2488953 |
| -0.2354278 | 0.3041720 | -0.7739955 | 0.4395067 | -0.8338645 | 0.3630089 |
| -0.4143421 | 0.3063917 | -1.3523283 | 0.1769044 | -1.0163704 | 0.1876862 |
| -0.4923245 | 0.2847184 | -1.7291627 | 0.0844582 | -1.0518506 | 0.0672017 |
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. ## # A tibble: 10 x 3
## # Groups: Author_year [10]
## Author_year Study_ID mean
## <chr> <fct> <dbl>
## 1 Alves_2017 F001 -0.0774
## 2 Barbosa_2018 F002 0.198
## 3 Bhavsar_2014 F003 0.153
## 4 DelGobbo_2008 F005 -2.00
## 5 Hu_2020 F006 -0.134
## 6 Kim_2020 F007 -0.887
## 7 Luo_2019 F008 -0.161
## 8 Sungur_2019 F010 -0.893
## 9 Taylor_2019 F011 0.214
## 10 Vassiliadou_2015 F013 0.672
Study_ID F005 (Del Gobbo et al. 2008)dat.sens <- filter(dat, Author_year != "DelGobbo_2008")
dat.sens <- as.data.frame(dat.sens) # convert data set into a data frame to calculate VCV matrix
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 |
Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), test = "t", data = dat.sens)
summary(mod.sens)##
## Multivariate Meta-Analysis Model (k = 430; method: REML)
##
## logLik Deviance AIC BIC AICc
## -262.2763 524.5525 536.5525 560.9073 536.7520
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2068 0.4547 8 no Study_ID
## sigma^2.2 0.0210 0.1449 22 no Species_common
## sigma^2.3 0.0950 0.3081 17 no PFAS_type
## sigma^2.4 0.0889 0.2981 430 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 428) = 2075.8092, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 105.2316, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.5875 0.2081 2.8223 428 0.0050 0.1783
## Length_cooking_time_in_s -0.0012 0.0001 -10.2582 428 <.0001 -0.0014
## ci.ub
## intrcpt 0.9966 **
## Length_cooking_time_in_s -0.0009 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s,
"Cooking time (s)")oil_dat.sens<-filter(dat.sens, Cooking_Category=="oil-based")
##### Oil based
full_model_oil_time.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_oil_time.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -101.3633 202.7266 220.7266 252.4914 221.4704
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0656 0.2561 5 no Study_ID
## sigma^2.2 0.0162 0.1274 15 no Species_common
## sigma^2.3 0.1154 0.3398 16 no PFAS_type
## sigma^2.4 0.0285 0.1687 257 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 543.8908, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 30.9152, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.5252 0.1889 2.7806 252 0.0058
## scale(Temperature_in_Celsius) -0.0228 0.0710 -0.3214 252 0.7482
## Length_cooking_time_in_s -0.0015 0.0001 -10.2654 252 <.0001
## scale(PFAS_carbon_chain) 0.1460 0.0739 1.9768 252 0.0492
## scale(log(Ratio_liquid_fish_0 + 1)) -0.4408 0.1569 -2.8095 252 0.0054
## ci.lb ci.ub
## intrcpt 0.1532 0.8972 **
## scale(Temperature_in_Celsius) -0.1626 0.1170
## Length_cooking_time_in_s -0.0018 -0.0012 ***
## scale(PFAS_carbon_chain) 0.0005 0.2915 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.7498 -0.1318 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")
##### Water based
water_dat.sens<-filter(dat.sens, Cooking_Category=="water-based")
full_model_water_time.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
summary(full_model_water_time.sens)##
## Multivariate Meta-Analysis Model (k = 101; method: REML)
##
## logLik Deviance AIC BIC AICc
## -59.6158 119.2315 135.2315 155.8292 136.8679
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.1409 0.3754 5 no Study_ID
## sigma^2.2 0.0000 0.0000 13 no Species_common
## sigma^2.3 0.1205 0.3472 15 no PFAS_type
## sigma^2.4 0.0654 0.2558 101 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 97) = 322.9711, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.3851, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt 0.3276 0.2725 1.2024 97 0.2321
## Length_cooking_time_in_s -0.0013 0.0002 -6.1733 97 <.0001
## scale(PFAS_carbon_chain) 0.1696 0.0824 2.0589 97 0.0422
## scale(log(Ratio_liquid_fish_0 + 1)) -0.2591 0.1547 -1.6745 97 0.0972
## ci.lb ci.ub
## intrcpt -0.2132 0.8683
## Length_cooking_time_in_s -0.0017 -0.0009 ***
## scale(PFAS_carbon_chain) 0.0061 0.3331 *
## scale(log(Ratio_liquid_fish_0 + 1)) -0.5662 0.0480 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")
##### No liquid
dry_dat.sens<-filter(dat.sens, Cooking_Category=="No liquid")
full_model_dry_time.sens<- run_model(dry_dat.sens, ~ Length_cooking_time_in_s)
pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")
ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(0,0),
legend.justification = c(0,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))dat.sens.vol0 <- filter(dat.sens, Ratio_liquid_fish_0 != "NA")
VCV_lnRR.sens.vol0 <- make_VCV_matrix(dat.sens.vol0, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.vol0 <- rma.mv(lnRR, VCV_lnRR.sens.vol0, mods = ~log(Ratio_liquid_fish_0 +
1), random = list(~1 | Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
test = "t", data = dat.sens.vol0)
summary(mod.sens.vol0)##
## Multivariate Meta-Analysis Model (k = 467; method: REML)
##
## logLik Deviance AIC BIC AICc
## -401.1340 802.2679 814.2679 839.1201 814.4513
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.3263 0.5712 7 no Study_ID
## sigma^2.2 0.0510 0.2259 27 no Species_common
## sigma^2.3 0.1373 0.3706 18 no PFAS_type
## sigma^2.4 0.1583 0.3978 467 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 465) = 2238.2075, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 465) = 2.8724, p-val = 0.0908
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt -0.0976 0.2520 -0.3872 465 0.6988 -0.5928
## log(Ratio_liquid_fish_0 + 1) -0.0425 0.0251 -1.6948 465 0.0908 -0.0919
## ci.ub
## intrcpt 0.3977
## log(Ratio_liquid_fish_0 + 1) 0.0068 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol0, mod.sens.vol0, log(dat.sens.vol0$Ratio_liquid_fish_0 +
1), "ln(Liquid volume to tissue sample ratio + 1) (mL/g)") + scale_fill_manual(values = c("#55C667FF",
"goldenrod2", "dodgerblue3"))##### Oil based
full_model_oil_vol.sens <- run_model(oil_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
summary(full_model_oil_vol.sens)##
## Multivariate Meta-Analysis Model (k = 257; method: REML)
##
## logLik Deviance AIC BIC AICc
## -101.3633 202.7266 220.7266 252.4914 221.4704
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0656 0.2561 5 no Study_ID
## sigma^2.2 0.0162 0.1274 15 no Species_common
## sigma^2.3 0.1154 0.3398 16 no PFAS_type
## sigma^2.4 0.0285 0.1687 257 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 252) = 543.8908, p-val < .0001
##
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 30.9152, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## intrcpt -0.1890 0.2225 -0.8496 252 0.3963
## scale(Temperature_in_Celsius) -0.0228 0.0710 -0.3214 252 0.7482
## scale(Length_cooking_time_in_s) -0.3866 0.0377 -10.2654 252 <.0001
## scale(PFAS_carbon_chain) 0.1460 0.0739 1.9768 252 0.0492
## log_Ratio_liquid_fish0 -0.2862 0.1019 -2.8095 252 0.0054
## ci.lb ci.ub
## intrcpt -0.6271 0.2491
## scale(Temperature_in_Celsius) -0.1626 0.1170
## scale(Length_cooking_time_in_s) -0.4608 -0.3124 ***
## scale(PFAS_carbon_chain) 0.0005 0.2915 *
## log_Ratio_liquid_fish0 -0.4868 -0.0856 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat.sens$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol.sens <- run_model(water_dat.sens, ~scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0,
0, water_dat.sens$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
lnRR = 0)The code runs, but can’t be knitted for some reason
ggplot(dat.sens, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5,
col = "dodgerblue") + col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5,
col = "goldenrod") + col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid/animal tissue ratio + 1) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2))dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID",
obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 |
Study_ID, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)##
## Multivariate Meta-Analysis Model (k = 486; method: REML)
##
## logLik Deviance AIC BIC AICc
## -432.7165 865.4329 877.4329 902.5254 877.6090
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.2942 0.5424 9 no Study_ID
## sigma^2.2 0.0772 0.2778 31 no Species_common
## sigma^2.3 0.0959 0.3098 18 no PFAS_type
## sigma^2.4 0.1860 0.4313 486 no Effect_ID
##
## Test for Residual Heterogeneity:
## QE(df = 484) = 3092.1464, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.1226, p-val = 0.2899
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.3223 0.3251 -0.9914 484 0.3220 -0.9612 0.3165
## PFAS_carbon_chain 0.0284 0.0268 1.0595 484 0.2899 -0.0242 0.0809
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length") # The relationship with cooking time appears even stronger#### Oil based
full_model_oil_PFAS.sens<- run_model(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS.sens<- run_model(water_dat.sens, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS.sens<- run_model(dry_dat.sens, ~ PFAS_carbon_chain)
pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")
ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_mod.sens <- run_model(dat.sens, ~-1 + Cooking_Category + scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
1)))
funnel(full_mod.sens, yaxis = "seinv")All figures in the publication were generated using estimates from models taking the liquid/animal tissue ratio as “0” for the dry cooking category.
full_model_time0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_time0<-predict.rma(full_model_time0, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time0<-as.data.frame(pred_full_model_time0)
pred_full_model_time0$Length_cooking_time_in_s=pred_full_model_time0$X.Length_cooking_time_in_s
pred_full_model_time0<-left_join(dat, pred_full_model_time0, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_vol0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish0)
pred_full_model_vol0<-predict.rma(full_model_vol0, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish0))
pred_full_model_vol0<-as.data.frame(pred_full_model_vol0)
pred_full_model_vol0$log_Ratio_liquid_fish0=pred_full_model_vol0$X.log_Ratio_liquid_fish
pred_full_model_vol0<- pred_full_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0)-1, lnRR = 0)
uni_model_vol0<- run_model(dat, ~ log_Ratio_liquid_fish0)
pred_uni_model_vol0<-predict.rma(uni_model_vol0, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol0<-as.data.frame(pred_uni_model_vol0)
pred_uni_model_vol0$log_Ratio_liquid_fish0=pred_uni_model_vol0$X.log_Ratio_liquid_fish
pred_uni_model_vol0<- pred_uni_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0) -1, lnRR = 0)
p_vol0<-ggplot(dat,aes(x = log_Ratio_liquid_fish0, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol0,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid/animal tissue ratio + 1) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_temp0<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_temp0<-predict.rma(full_model_temp0, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp0<-as.data.frame(pred_full_model_temp0)
pred_full_model_temp0$Temperature_in_Celsius=pred_full_model_temp0$X.Temperature_in_Celsius
pred_full_model_temp0<-left_join(dat, pred_full_model_temp0, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_PFAS0<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_full_model_PFAS0<-predict.rma(full_model_PFAS0, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS0<-as.data.frame(pred_full_model_PFAS0)
pred_full_model_PFAS0$PFAS_carbon_chain=pred_full_model_PFAS0$X.PFAS_carbon_chain
pred_full_model_PFAS0<-left_join(dat, pred_full_model_PFAS0, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS0,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_time0 + p_vol0)/(p_temp0 + p_PFAS0) + plot_annotation(tag_levels = c("A", "B",
"C", "D"))ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)my_orchard<- function (object, mod = "1", group, data, xlab, N = "none",
alpha = 0.5, angle = 90, cb = FALSE, k = TRUE, g = TRUE,
trunk.size = 3, branch.size = 1.2, twig.size = 0.5, transfm = c("none",
"tanh"), condition.lab = "Condition", legend.pos = c("bottom.right",
"bottom.left", "top.right", "top.left",
"top.out", "bottom.out"), k.pos = c("right",
"left"), weights = "prop", by = NULL, at = NULL)
{
transfm <- match.arg(NULL, choices = transfm)
legend.pos <- match.arg(NULL, choices = legend.pos)
k.pos <- match.arg(NULL, choices = k.pos)
if (any(class(object) %in% c("rma.mv", "rma"))) {
if (mod != "1") {
results <- orchaRd::mod_results(object, mod, group,
data, by = by, at = at, weights = weights)
}
else {
results <- orchaRd::mod_results(object, mod = "1",
group, data, by = by, at = at, weights = weights)
}
}
if (any(class(object) %in% c("orchard"))) {
results <- object
}
mod_table <- results$mod_table
data_trim <- results$data
data_trim$moderator <- factor(data_trim$moderator, levels = mod_table$name,
labels = mod_table$name)
data_trim$scale <- (1/sqrt(data_trim[, "vi"]))
legend <- "Precision (1/SE)"
if (any(N != "none")) {
data_trim$scale <- N
legend <- paste0("Sample Size (", "N", ")")
}
if (transfm == "tanh") {
cols <- sapply(mod_table, is.numeric)
mod_table[, cols] <- Zr_to_r(mod_table[, cols])
data_trim$yi <- Zr_to_r(data_trim$yi)
label <- xlab
}
else {
label <- xlab
}
mod_table$K <- as.vector(by(data_trim, data_trim[, "moderator"],
function(x) length(x[, "yi"])))
mod_table$g <- as.vector(num_studies(data_trim, moderator,
stdy)[, 2])
group_no <- length(unique(mod_table[, "name"]))
cbpl <- c("#88CCEE", "#CC6677", "#DDCC77",
"#117733", "#332288", "#AA4499", "#44AA99",
"#999933", "#882255", "#661100", "#6699CC",
"#888888", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7",
"#999999")
if (names(mod_table)[2] == "condition") {
condition_no <- length(unique(mod_table[, "condition"]))
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data_trim,
ggplot2::aes(y = yi, x = moderator, size = scale,
colour = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerCL, ymax = upperCL), size = branch.size,
position = ggplot2::position_dodge2(width = 0.3)) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerPR, ymax = upperPR, shape = as.factor(condition),
fill = name), size = twig.size, stroke=2.2,position = ggplot2::position_dodge2(width = 0.3), # Added stroke
fatten = trunk.size) + ggplot2::scale_shape_manual(values = 20 +
(1:condition_no)) + ggplot2::coord_flip() + ggplot2::theme_bw() +
ggplot2::guides(fill = "none", colour = "none") +
ggplot2::theme(legend.position = c(0, 1), legend.justification = c(0,
1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend,
parse = TRUE) + ggplot2::labs(shape = condition.lab) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
else {
plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data_trim,
ggplot2::aes(y = yi, x = moderator, size = scale,
colour = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0,
linetype = 2, colour = "black", alpha = alpha) +
ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name,
ymin = lowerCL, ymax = upperCL), size = branch.size) +
ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate,
x = name, ymin = lowerPR, ymax = upperPR, fill = name),
size = twig.size, fatten = trunk.size, shape = 21) +
ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none",
colour = "none") + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) +
ggplot2::theme(legend.direction = "horizontal") +
ggplot2::theme(legend.background = ggplot2::element_blank()) +
ggplot2::labs(y = label, x = "", size = legend) +
ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10,
colour = "black", hjust = 0.5, angle = angle))
}
if (legend.pos == "bottom.right") {
plot <- plot + ggplot2::theme(legend.position = c(1,
0), legend.justification = c(1, 0))
}
else if (legend.pos == "bottom.left") {
plot <- plot + ggplot2::theme(legend.position = c(0,
0), legend.justification = c(0, 0))
}
else if (legend.pos == "top.right") {
plot <- plot + ggplot2::theme(legend.position = c(1,
1), legend.justification = c(1, 1))
}
else if (legend.pos == "top.left") {
plot <- plot + ggplot2::theme(legend.position = c(0,
1), legend.justification = c(0, 1))
}
else if (legend.pos == "top.out") {
plot <- plot + ggplot2::theme(legend.position = "top")
}
else if (legend.pos == "bottom.out") {
plot <- plot + ggplot2::theme(legend.position = "bottom")
}
if (cb == TRUE) {
plot <- plot + ggplot2::scale_fill_manual(values = cbpl) +
ggplot2::scale_colour_manual(values = cbpl)
}
if (k == TRUE && g == FALSE && k.pos == "right") {
plot <- plot + ggplot2::annotate("text", y = (max(data_trim$yi) +
(max(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "right", size = 3.5)
}
else if (k == TRUE && g == FALSE && k.pos == "left") {
plot <- plot + ggplot2::annotate("text", y = (min(data_trim$yi) +
(min(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no]),
parse = TRUE, hjust = "left", size = 3.5)
}
else if (k == TRUE && g == TRUE && k.pos == "right") {
plot <- plot + ggplot2::annotate("text", y = (max(data_trim$yi) +
(max(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no],
" (", mod_table$g[1:group_no], ")"),
parse = TRUE, hjust = "right", size = 3.5)
}
else if (k == TRUE && g == TRUE && k.pos == "left") {
plot <- plot + ggplot2::annotate("text", y = (min(data_trim$yi) +
(min(data_trim$yi) * 0.1)), x = (seq(1, group_no,
1) + 0.3), label = paste("italic(k)==", mod_table$K[1:group_no],
" (", mod_table$g[1:group_no], ")"),
parse = TRUE, hjust = "left", size = 3.5)
}
return(plot)
}full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)
# full model with Ratio_liquid_fish taken as `0` for the dry cooking category
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)
# full model without the 'No liquid' data for figure 3B, when Ratio_liquid_fish
# is taken as `NA` for the dry cooking category
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)Estimates at cooking times of 2, 10 and 25 min
time_mm0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm0<-my_orchard(time_mm0, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10), breaks=c(2,4,6))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431), parse = TRUE, hjust = "right", size = 3.5) +
annotate("text", y = 2.3, x = 1.299, label = paste("(7)"), parse = TRUE, hjust = "right", size = 3.5) Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm0 <-mod_results(full_model_org_units0, data = dat, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish0= c(0, 2.4, 3.8)), by = "log_Ratio_liquid_fish0")
p_volume_mm0<-my_orchard(volume_mm0, xlab = "lnRR", condition.lab = "ln (Liquid/animal tissue ratio + 1) (mL/g)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431), parse = TRUE, hjust = "right", size = 3.5) +
annotate("text", y = 2.3, x = 1.297, label = paste("(7)"), parse = TRUE, hjust = "right", size = 3.5) Estimates at cooking times of 2, 10 and 25 min
In this case, water- and oil-based cooking must be separated from dry cooking to avoid extrapolations of the dry cooking effect sizes at the mean liquid ratio.
time_mm_cat <- mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_wat_oil<-my_orchard(time_mm_cat ,xlab = "lnRR", group="Study_ID", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 0), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
legend.position = "none")+
guides(shape=F, size=F)+
ylim(-6.1, 3)+
annotate("text", y = 1.9, x = (seq(1, 2, 1) + 0.301), label = paste("italic(k)==", c(263, 121)), parse = TRUE, hjust = "right", size = 3.5)+
annotate("text", y = 2.3, x = (seq(1, 2, 1) + 0.3), label = paste(c("(6)", "(6)")), parse = TRUE, hjust = "right", size = 3.5)
time_mm_dry<-mod_results(full_model_org_units_dry, data = dat_dry, group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_dry<-my_orchard(time_mm_dry, xlab = "lnRR", group="Study_ID", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left", k=F)+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values=c("#55C667FF"))+
scale_colour_manual(values = c("#55C667FF"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 12),
legend.text = element_text(size = 10))+
guides(size=F)+
ylim(-6.05, 3)+
annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 47), parse = TRUE, hjust = "right", size = 3.5)+
annotate("text", y = 2.3, x = 1.299, label = paste("(1)"), parse = TRUE, hjust = "right", size = 3.5)
p_time_mm_cat<-p_time_mm_wat_oil/p_time_mm_dry + plot_layout(heights=c(2,1))((p_time_mm0/p_volume_mm0) | p_time_mm_cat) + plot_annotation(tag_levels = c("A",
"B", "C"))ggsave("fig/Fig_3.png", width = 14, height = 11, dpi = 1200)##### Oil based
full_model_oil_time0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)##### Oil based
full_model_oil_vol0 <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_oil_vol0 <- predict.rma(full_model_oil_vol0, addx = TRUE, newmods = cbind(0,
0, 0, oil_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol0 <- as.data.frame(pred_oil_vol0)
pred_oil_vol0 <- pred_oil_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol0 <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_water_vol0 <- predict.rma(full_model_water_vol0, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol0 <- as.data.frame(pred_water_vol0)
pred_water_vol0 <- pred_water_vol0 %>%
mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
lnRR = 0)
p_4B0 <- ggplot(dat, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol0, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.3) + geom_line(data = pred_oil_vol0, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid/animal tissue ratio + 1) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2))full_model_oil_temp0<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_temp0<-predict.rma(full_model_oil_temp0, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp0<-as.data.frame(pred_oil_temp0)
pred_oil_temp0$Temperature_in_Celsius=pred_oil_temp0$X.Temperature_in_Celsius
pred_oil_temp0<-left_join(oil_dat, pred_oil_temp0, by="Temperature_in_Celsius")
p_4C0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"),
labels=c("no liquid", "oil-based", "water-based"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)##### Oil based
full_model_oil_PFAS0<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS0<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish_0 + 1)))
pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_4A0 + p_4B0)/(p_4C0 + p_4D0) + plot_annotation(tag_levels = c("A", "B", "C", "D"))ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)dat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f0 <- funnel(full_model0,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=2,
ylim=c(0.85,1.05),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)p_5A0 <- recordPlot(plot_f0)
invisible(dev.off())full_model_egger0 <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_egger0<-predict.rma(full_model_egger0, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger0<-as.data.frame(pred_egger0)
pred_egger0$SE_eff_N=pred_egger0$X.I.sqrt.1.N_tilde..
pred_egger0<- pred_egger0 %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B0<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)full_model_pub0 <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions
pred_pub0<-predict.rma(full_model_pub0, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub0<-as.data.frame(pred_pub0)
pred_pub0$Publication_year=pred_pub0$X.Publication_year
pred_pub0<-left_join(dat, pred_pub0, by="Publication_year")
p_5C0<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub0,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))(ggdraw(p_5A0) + ggdraw(p_5B0) + ggdraw(p_5C0) + plot_annotation(tag_levels = "A"))ggsave(here("fig/Fig_5BC.png"), width = 18, height = 7, dpi = 1200)In those figures, the liquid/animal tissue ratio was taken as NA for the dry cooking category
NA for the dry cooking methodfull_model_time<- run_model(dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")
uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)
pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")
p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_vol<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
log_Ratio_liquid_fish)
pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)
pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0)
p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "ln (Liquid volume to tissue sample ratio) (mL/g)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_temp<- run_model(dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")
uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)
pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")
p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))full_model_PFAS<- run_model(dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")
uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)
pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")
p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+
geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C",
"D"))NA for the dry cooking categoryEstimates at cooking times of 2, 10 and 25 min
time_mm <-mod_results(full_model_org_units, data = dat, mod = "1", group="Study_ID", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", mod="1", condition.lab = "Cooking time (sec)", group="Study_ID", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)Estimates at 0 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue
volume_mm <-mod_results(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", group="Study_ID",at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", data=dat_oil_water,condition.lab = "ln (Liquid to sample ratio)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10))+
scale_fill_manual(values="gray75")+
scale_colour_manual(values = "gray60")+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))+
guides(size=F)Estimates at cooking times of 2, 10 and 25 min
time_mm_cat <- mod_results(full_model_org_units, data = dat, mod = "Cooking_Category", group="Study_ID",at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3, trunk.size=8, branch.size = 1.75, twig.size = 0.75, legend.pos="bottom.left")+
scale_size_continuous(range = c(1, 10), breaks=c(2,4,6))+
scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 13),
legend.text = element_text(size = 10))((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B",
"C"))NA for the dry cooking category##### Oil based
full_model_oil_time<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")
##### Water based
full_model_water_time<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
Length_cooking_time_in_s+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")
##### No liquid
full_model_dry_time<- run_model(dry_dat, ~ Length_cooking_time_in_s)
pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")
p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+
geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="horizontal",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(fill=F)##### Oil based
full_model_oil_vol <- run_model(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
0, oil_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
lnRR = 0) # for the plot to work, we need to add a column with cooking category and a column with lnRR
##### Water based
full_model_water_vol <- run_model(water_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
0, water_dat$log_Ratio_liquid_fish)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
lnRR = 0)
p_4B <- ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio) (mL/g)",
y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
size = 1.2)) #### The line doesn't go all the way down for water-based because the highest values are not included in the full modelfull_model_oil_temp<- run_model(oil_dat, ~ Temperature_in_Celsius +
scale(Length_cooking_time_in_s)+
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish)))
pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0))
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")
p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
guides(size=F)##### Oil based
full_model_oil_PFAS<- run_model(oil_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")
##### Water based
full_model_water_PFAS<- run_model(water_dat, ~ scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s)+
PFAS_carbon_chain +
scale(log(Ratio_liquid_fish)))
pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")
##### No liquid
full_model_dry_PFAS<- run_model(dry_dat, ~ PFAS_carbon_chain)
pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")
p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+
geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+
geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+
geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))NA for the dry cooking categorydat$Study_ID<- as.factor(dat$Study_ID)
# funnel(full_model,
# yaxis="seinv", # Inverse of standard error (precision) as the y axis
# level = c(90, 95, 99), # levels of statistical significance highlighted
# shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
# legend = TRUE, # display legend
# ylab="Precision (1/SE)",
# cex.lab=1.75,
# digits=1,
# cex=2,
# pch=21,
# col=dat$Study_ID)
pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))
plot_f <- funnel(full_model,
yaxis="seinv", # Inverse of standard error (precision) as the y axis
level = c(90, 95, 99), # levels of statistical significance highlighted
shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
legend = TRUE, # display legend
ylab="Precision (1/SE)",
cex.lab=1.75,
digits=2,
ylim=c(0.84,1.02),
xlim=c(-6, 6),
cex=2,
pch=21,
col=dat$Study_ID)p_5A <- recordPlot(plot_f)
invisible(dev.off())full_model_egger <- run_model(dat, ~ - 1 +
I(sqrt(1/N_tilde)) +
scale(Publication_year) +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0))
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0)
p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.position="none",
panel.border=element_rect(colour="black", fill=NA, size=1.2))+
xlim(0.18,1)full_model_pub <- run_model(dat, ~ - 1 +
scale(I(sqrt(1/N_tilde))) +
Publication_year +
scale(Temperature_in_Celsius) +
scale(Length_cooking_time_in_s) +
scale(PFAS_carbon_chain) +
scale(log(Ratio_liquid_fish))) # Model to get predictions
pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0))
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")
p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+
labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") +
scale_size_continuous(range=c(1,10))+
theme_bw() +
geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+ # horizontal line at lnRR = 0
theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
legend.text=element_text(size=14),
legend.position=c(1,0),
legend.justification = c(1,0),
legend.background = element_blank(),
legend.direction="vertical",
legend.title = element_text(size=15),
panel.border=element_rect(colour="black", fill=NA, size=1.2)) +
scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = "A"))sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Australia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] cowplot_1.1.1 GGally_2.1.2 kableExtra_1.3.4
## [4] emmeans_1.7.2-9000003 patchwork_1.1.1 clubSandwich_0.5.3
## [7] ape_5.5 orchaRd_2.0 metaAidR_0.0.0.9000
## [10] metafor_3.0-2 Matrix_1.3-4 here_1.0.1
## [13] googlesheets4_1.0.0 forcats_0.5.1 stringr_1.4.0
## [16] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
## [19] tidyr_1.1.3 tibble_3.1.3 ggplot2_3.3.5
## [22] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 googledrive_2.0.0 ggbeeswarm_0.6.0 colorspace_2.0-2
## [5] ellipsis_0.3.2 rprojroot_2.0.2 estimability_1.3 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.1.0 fansi_0.5.0 mvtnorm_1.1-3
## [13] lubridate_1.8.0 mathjaxr_1.4-0 xml2_1.3.3 codetools_0.2-18
## [17] splines_4.1.0 knitr_1.37 jsonlite_1.7.2 broom_0.7.11
## [21] dbplyr_2.1.1 compiler_4.1.0 httr_1.4.2 backports_1.4.1
## [25] assertthat_0.2.1 gargle_1.2.0 cli_3.0.1 formatR_1.11
## [29] htmltools_0.5.1.1 tools_4.1.0 coda_0.19-4 gtable_0.3.0
## [33] glue_1.4.2 Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.4
## [37] vctrs_0.3.8 svglite_2.0.0 nlme_3.1-152 xfun_0.29
## [41] rvest_1.0.2 lifecycle_1.0.1 MASS_7.3-54 zoo_1.8-9
## [45] scales_1.1.1 hms_1.1.1 parallel_4.1.0 sandwich_3.0-1
## [49] RColorBrewer_1.1-2 yaml_2.2.1 sass_0.4.0 reshape_0.8.8
## [53] stringi_1.7.6 highr_0.9 rlang_0.4.11 pkgconfig_2.0.3
## [57] systemfonts_1.0.2 evaluate_0.14 lattice_0.20-44 labeling_0.4.2
## [61] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1 bookdown_0.22
## [65] R6_2.5.1 generics_0.1.1 multcomp_1.4-17 DBI_1.1.2
## [69] pillar_1.6.5 haven_2.4.3 withr_2.4.3 survival_3.2-11
## [73] modelr_0.1.8 crayon_1.4.2 utf8_1.2.2 tzdb_0.2.0
## [77] rmarkdown_2.11 grid_4.1.0 readxl_1.3.1 rmdformats_1.0.2
## [81] reprex_2.0.1 digest_0.6.27 webshot_0.5.2 xtable_1.8-4
## [85] munsell_0.5.0 beeswarm_0.4.0 viridisLite_0.4.0 vipor_0.4.5
## [89] bslib_0.2.5.1